Building management system with virtual points and optimized data integration

ABSTRACT

A building management system (BMS) includes building equipment, a data collector, a sample aggregator, and an application. The building equipment is operable to monitor and control a variable in the BMS and to provide raw data samples of the variable. The data collector is configured to collect the raw data samples from the building equipment and generate a raw data timeseries including a plurality of the raw data samples. The sample aggregator is configured to automatically generate a data rollup timeseries including a plurality of aggregated data samples. The aggregated data samples are generated by aggregating the raw data samples as the raw data samples are collected from the building equipment. Both timeseries are stored in a timeseries database. The application is configured to retrieve the raw data timeseries and the data rollup timeseries from the timeseries database in response to a request for timeseries data associated with the variable.

This application is a continuation of U.S. application Ser. No.15/182,580 filed Jun. 14, 2016, the entirety of which is incorporated byreference herein.

BACKGROUND

The present disclosure relates generally to the field of buildingmanagement systems. A building management system (BMS) is, in general, asystem of devices configured to control, monitor, and manage equipmentin or around a building or building area. A BMS can include, forexample, a HVAC system, a security system, a lighting system, a firealerting system, any other system that is capable of managing buildingfunctions or devices, or any combination thereof.

A BMS can collect data from sensors and other types of buildingequipment. Data can be collected over time and combined into streams oftimeseries data. Each sample of the timeseries data can include atimestamp and a data value. Some BMSs store raw timeseries data in arelational database without significant organization or processing atthe time of data collection. Applications that consume the timeseriesdata are typically responsible for retrieving the raw timeseries datafrom the database and generating views of the timeseries data that canbe presented via a chart, graph, or other user interface. Theseprocessing steps are typically performed in response to a request forthe timeseries data, which can significantly delay data presentation atquery time.

SUMMARY

One implementation of the present disclosure is a building managementsystem (BMS). The BMS includes building equipment, a data collector, asample aggregator, and an application. The building equipment areoperable to monitor and control a variable in the BMS and to provide rawdata samples of the variable. The data collector is configured tocollect the raw data samples from the building equipment, generate a rawdata timeseries including a plurality of the raw data samples, and storethe raw data timeseries in a timeseries database. The sample aggregatoris configured to automatically generate a data rollup timeseriesincluding a plurality of aggregated data samples. The aggregated datasamples are generated by aggregating the raw data samples as the rawdata samples are collected from the building equipment. The sampleaggregator is configured to store the data rollup timeseries in thetimeseries database. The application is configured to retrieve the rawdata timeseries and the data rollup timeseries from the timeseriesdatabase in response to a request for timeseries data associated withthe variable.

In some embodiments, the data collector is configured to store each ofthe raw data samples with a timestamp. The timestamp can include a localtime indicating a time at which the raw data sample was collected in atime zone within which the raw data sample was collected. The timestampcan also include a time offset indicating a difference between the localtime and universal time.

In some embodiments, each of the raw data samples includes a timestampand a raw data value. The sample aggregator can be configured togenerate each of the aggregated data samples by aggregating one or moreof the raw data samples that have timestamps within a predeterminedaggregation interval. In some embodiments, aggregating the one or moreraw data samples includes averaging the raw data values of the one ormore raw data samples.

In some embodiments, the sample aggregator is configured toautomatically update the data rollup timeseries each time a new raw datasample is collected from the building equipment. In some embodiments,the sample aggregator is configured to automatically update the datarollup timeseries by identifying a timestamp of the new raw data sample,identifying an aggregated data sample of the data rollup timeseries thatwas generated using an aggregation interval that includes the timestampof the new raw data sample, and recalculating an aggregated data valueof the identified aggregated data sample using the new raw data sampleand any other raw data samples that have timestamps within theaggregation interval.

In some embodiments, the sample aggregator is configured toautomatically update the data rollup timeseries by identifying atimestamp of the new raw data sample, determining that the timestamp ofthe new raw data sample is not within any aggregation interval used togenerate the plurality of aggregated data samples, generating a newaggregated data sample using the new raw data sample and a newaggregation interval that includes the timestamp of the raw data sample,and adding the new aggregated data sample to the data rollup timeseries.

In some embodiments, the sample aggregator is configured to perform adata cleansing operation on the raw data timeseries before using the rawdata timeseries to generate the data rollup timeseries.

In some embodiments, each of the aggregated data samples includes atimestamp and an aggregated data value. The sample aggregator can beconfigured to generate a second data rollup timeseries by aggregatingone or more of the aggregated data samples that have timestamps within asecond predetermined aggregation interval.

In some embodiments, the BMS includes a virtual point calculatorconfigured to create a virtual data point representing a variable notdirectly measured by the building equipment, calculate data values foreach of a plurality of samples of the virtual data point using at leastone of the raw data samples and the aggregated data samples, generate avirtual point timeseries including the plurality of samples of thevirtual data point, and store the virtual point timeseries in thetimeseries database.

In some embodiments, the BMS includes a scalable rules engine configuredto detect faults in the timeseries data by applying fault detectionrules to at least one of the raw data timeseries and the data rolluptimeseries. The scalable rules engine can generate a fault detectiontimeseries including a plurality of fault detection data samples. Eachof the fault detection data samples can have a timestamp and a datavalue indicating whether a fault is detected at the timestamp. Thescalable rules engine can store the fault detection timeseries in thetimeseries database.

Another implementation of the present disclosure is a buildingmanagement system (BMS). The BMS includes a sensor, a data collector, avirtual point calculator, a timeseries database, and an application. Thesensor is configured to measure a variable in the BMS and to provide rawdata samples of the measured variable. The data collector is configuredto collect the raw data samples from the sensor, generate a raw datatimeseries including a plurality of the raw data samples, and associatethe raw data timeseries with a measured data point. The virtual pointcalculator is configured to create a virtual data point representing anon-measured variable, calculate the virtual data point as a function ofthe measured data point, and generate a virtual point timeseriesincluding a plurality of samples of the virtual data point. Thetimeseries database is configured to store the raw data timeseries andthe virtual point timeseries. The application is configured to retrievethe virtual point timeseries from the timeseries database in response toa request for timeseries data associated with the virtual data point.

In some embodiments, the application is configured to handle both theraw data timeseries and the virtual point timeseries in the same manner,regardless of whether the timeseries is associated with a measured datapoint or a virtual data point.

In some embodiments, the virtual point calculator is configured tocalculate the virtual data point by applying values of the measured datapoint as inputs to a mathematical function and evaluating themathematical function to determine corresponding values of the virtualdata point. In some embodiments, the virtual point calculator isconfigured to calculate the virtual data point as a function of themeasured data point and one or more other data points.

In some embodiments, the BMS includes a sample aggregator configured togenerate a data rollup timeseries including a plurality of aggregateddata samples. The sample aggregator can calculate a value for each ofthe aggregated data samples by aggregating one or more of the raw datasamples that have timestamps within a predetermined aggregationinterval.

In some embodiments, the sample aggregator is configured to synchronizethe raw data timeseries with an asynchronous timeseries by aggregatingboth the raw data timeseries and the asynchronous timeseries usingequivalent aggregation intervals. In some embodiments, the virtual pointcalculator is configured to calculate the virtual data point byidentifying a plurality of aggregated data values generated byaggregating the raw data timeseries. The virtual point calculator canidentify, for each of the aggregated data values, a correspondingsynchronized data value generated by aggregating the asynchronoustimeseries. The virtual point calculator can calculate, for each sampleof the virtual data point, a data value of the sample by evaluating afunction of one of the aggregated data values and the correspondingsynchronized data value.

In some embodiments, the measured variable is a weather-relatedvariable. The data collector can be configured to associate the raw datatimeseries with a measured weather-related data point. The virtual pointcalculator can include a weather point calculator configured tocalculate the virtual data point as a function of the measuredweather-related data point.

In some embodiments, the BMS includes a scalable rules engine configuredto detect faults in the timeseries data by applying fault detectionrules to the virtual point timeseries. The scalable rules engine cangenerate a fault detection timeseries including a plurality of faultdetection data samples. Each of the fault detection data samples canhave a timestamp and a data value indicating whether a fault is detectedat the timestamp. The scalable rules engine can store the faultdetection timeseries in the timeseries database.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing of a building equipped with a building managementsystem (BMS) and a HVAC system, according to some embodiments.

FIG. 2 is a schematic of a waterside system which can be used as part ofthe HVAC system of FIG. 1, according to some embodiments.

FIG. 3 is a block diagram of an airside system which can be used as partof the HVAC system of FIG. 1, according to some embodiments.

FIG. 4 is a block diagram of a BMS which can be used in the building ofFIG. 1, according to some embodiments.

FIG. 5 is a block diagram of another BMS which can be used in thebuilding of FIG. 1. The BMS is shown to include a data collector, dataplatform services, applications, and a dashboard layout generator,according to some embodiments.

FIG. 6 is a block diagram of a timeseries service and an analyticsservice which can be implemented as some of the data platform servicesshown in FIG. 5, according to some embodiments.

FIG. 7A is a block diagram illustrating an aggregation technique whichcan be used by the sample aggregator shown in FIG. 6 to aggregate rawdata samples, according to some embodiments.

FIG. 7B is a data table which can be used to store raw data timeseriesand a variety of optimized data timeseries which can be generated by thetimeseries service of FIG. 6, according to some embodiments.

FIG. 8 is a drawing of several timeseries illustrating thesynchronization of data samples which can be performed by the dataaggregator shown in FIG. 6, according to some embodiments.

FIG. 9A is a flow diagram illustrating the creation and storage of afault detection timeseries which can be performed by the job managershown in FIG. 6, according to some embodiments.

FIG. 9B is a data table which can be used to store the raw datatimeseries and the fault detection timeseries, according to someembodiments.

FIG. 9C is a flow diagram illustrating how various timeseries can begenerated, stored, and used by the data platform services of FIG. 5,according to some embodiments.

FIG. 10A is an entity graph illustrating relationships between anorganization, a space, a system, a point, and a timeseries, which can beused by the data collector of FIG. 5, according to some embodiments.

FIG. 10B is an example of an entity graph for a particular buildingmanagement system according to some embodiments.

FIG. 11 is an object relationship diagram illustrating relationshipsbetween an entity template, a point, a timeseries, and a data sample,which can be used by the data collector of FIG. 5 and the timeseriesservice of FIG. 6, according to some embodiments.

FIG. 12 is a flow diagram illustrating the operation of the dashboardlayout generator of FIG. 5, according to some embodiments.

FIG. 13 is a grid illustrating dashboard layout description which can begenerated by the dashboard layout generator of FIG. 5, according to someembodiments.

FIG. 14 is an example of object code describing a dashboard layout whichcan be generated by the dashboard layout generator of FIG. 5, accordingto some embodiments.

FIG. 15 is a user interface illustrating a dashboard layout which can begenerated from the dashboard layout description of FIG. 14, according tosome embodiments.

FIG. 16 is another example of object code describing another dashboardlayout which can be generated by the dashboard layout generator of FIG.5, according to some embodiments.

FIG. 17 is a user interface illustrating a dashboard layout which can begenerated from the dashboard layout description of FIG. 16, according tosome embodiments.

DETAILED DESCRIPTION Overview

Referring generally to the FIGURES, a building management system (BMS)with virtual data points, optimized data integration, and aframework-agnostic dashboard layout is shown, according to variousembodiments. The BMS is configured to collect data samples from buildingequipment (e.g., sensors, controllable devices, building subsystems,etc.) and generate raw timeseries data from the data samples. The BMScan process the raw timeseries data using a variety of data platformservices to generate optimized timeseries data (e.g., data rolluptimeseries, virtual point timeseries, fault detection timeseries, etc.).The optimized timeseries data can be provided to various applicationsand/or stored in local or hosted storage. In some embodiments, the BMSincludes three different layers that separate (1) data collection, (2)data storage, retrieval, and analysis, and (3) data visualization. Thisallows the BMS to support a variety of applications that use theoptimized timeseries data and allows new applications to reuse theinfrastructure provided by the data platform services.

In some embodiments, the BMS includes a data collector configured tocollect raw data samples from the building equipment. The data collectorcan generate a raw data timeseries including a plurality of the raw datasamples and store the raw data timeseries in the timeseries database. Insome embodiments, the data collector stores each of the raw data sampleswith a timestamp. The timestamp can include a local time indicating thetime at which the raw data sample was collected in whichever time zonethe raw data sample was collected. The timestamp can also include a timeoffset indicating a difference between the local time and universaltime. The combination of the local timestamp and the offset provides aunique timestamp across daylight saving time boundaries. This allows anapplication using the timeseries data to display the timeseries data inlocal time without first converting from universal time. The combinationof the local timestamp and the offset also provides enough informationto convert the local timestamp to universal time without needing to lookup a schedule of when daylight savings time occurs.

In some embodiments, the data platform services include a sampleaggregator. The sample aggregator can aggregate predefined intervals ofthe raw timeseries data (e.g., quarter-hourly intervals, hourlyintervals, daily intervals, monthly intervals, etc.) to generate newoptimized timeseries of the aggregated values. These optimizedtimeseries can be referred to as “data rollups” since they are condensedversions of the raw timeseries data. The data rollups generated by thedata aggregator provide an efficient mechanism for various applicationsto query the timeseries data. For example, the applications canconstruct visualizations of the timeseries data (e.g., charts, graphs,etc.) using the pre-aggregated data rollups instead of the rawtimeseries data. This allows the applications to simply retrieve andpresent the pre-aggregated data rollups without requiring applicationsto perform an aggregation in response to the query. Since the datarollups are pre-aggregated, the applications can present the datarollups quickly and efficiently without requiring additional processingat query time to generate aggregated timeseries values.

In some embodiments, the data platform services include a virtual pointcalculator. The virtual point calculator can calculate virtual pointsbased on the raw timeseries data and/or the optimized timeseries data.Virtual points can be calculated by applying any of a variety ofmathematical operations (e.g., addition, subtraction, multiplication,division, etc.) or functions (e.g., average value, maximum value,minimum value, thermodynamic functions, linear functions, nonlinearfunctions, etc.) to the actual data points represented by the timeseriesdata. For example, the virtual point calculator can calculate a virtualdata point (pointID₃) by adding two or more actual data points (pointID₁and pointID₂) (e.g., pointID₃=pointID₁+pointID₂). As another example,the virtual point calculator can calculate an enthalpy data point(pointID₄) based on a measured temperature data point (pointID₅) and ameasured pressure data point (pointID₆) (e.g.,pointID₄=enthalpy(pointID₅, pointID₆)). The virtual data points can bestored as optimized timeseries data.

Applications can access and use the virtual data points in the samemanner as the actual data points. The applications do not need to knowwhether a data point is an actual data point or a virtual data pointsince both types of data points can be stored as optimized timeseriesdata and can be handled in the same manner by the applications. In someembodiments, the optimized timeseries data are stored with attributesdesignating each data point as either a virtual data point or an actualdata point. Such attributes allow the applications to identify whether agiven timeseries represents a virtual data point or an actual datapoint, even though both types of data points can be handled in the samemanner by the applications.

In some embodiments, the data platform services include a scalable rulesengine and/or an analytics service configured to analyze the timeseriesdata to detect faults. Fault detection can be performed by applying aset of fault detection rules to the timeseries data to determine whethera fault is detected at each interval of the timeseries. Fault detectionscan be stored as optimized timeseries data. For example, new timeseriescan be generated with data values that indicate whether a fault wasdetected at each interval of the timeseries. The time series of faultdetections can be stored along with the raw timeseries data and/oroptimized timeseries data in local or hosted data storage.

In some embodiments, the BMS includes a dashboard layout generator. Thedashboard layout generator is configured to generate a layout for a userinterface (i.e., a dashboard) visualizing the timeseries data. In someembodiments, the dashboard layout is not itself a user interface, butrather a description which can be used by applications to generate theuser interface. In some embodiments, the dashboard layout is a schemathat defines the relative locations of various widgets (e.g., charts,graphs, etc.) which can be rendered and displayed as part of the userinterface. The dashboard layout can be read by a variety of differentframeworks and can be used by a variety of different rendering engines(e.g., a web browser, a pdf engine, etc.) or applications to generatethe user interface.

In some embodiments, the dashboard layout defines a grid having one ormore rows and one or more columns located within each row. The dashboardlayout can define the location of each widget at a particular locationwithin the grid. The dashboard layout can define an array of objects(e.g., JSON objects), each of which is itself an array. In someembodiments, the dashboard layout defines attributes or properties ofeach widget. For example, the dashboard layout can define the type ofwidget (e.g., graph, plain text, image, etc.). If the widget is a graph,the dashboard layout can define additional properties such as graphtitle, x-axis title, y-axis title, and the timeseries data used in thegraph. These and other features of the building management system aredescribed in greater detail below.

Building Management System and HVAC System

Referring now to FIGS. 1-4, an exemplary building management system(BMS) and HVAC system in which the systems and methods of the presentdisclosure can be implemented are shown, according to an exemplaryembodiment. Referring particularly to FIG. 1, a perspective view of abuilding 10 is shown. Building 10 is served by a BMS. A BMS is, ingeneral, a system of devices configured to control, monitor, and manageequipment in or around a building or building area. A BMS can include,for example, a HVAC system, a security system, a lighting system, a firealerting system, any other system that is capable of managing buildingfunctions or devices, or any combination thereof.

The BMS that serves building 10 includes an HVAC system 100. HVAC system100 can include a plurality of HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 canprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 can use the heated or chilled fluid toheat or cool an airflow provided to building 10. An exemplary watersidesystem and airside system which can be used in HVAC system 100 aredescribed in greater detail with reference to FIGS. 2-3.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 can use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and can circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 can be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 can add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 can place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104can be transported to AHU 106 via piping 108.

AHU 106 can place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 can transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid can then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 can deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and canprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 can receive input from sensorslocated within AHU 106 and/or within the building zone and can adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Referring now to FIG. 2, a block diagram of a waterside system 200 isshown, according to an exemplary embodiment. In various embodiments,waterside system 200 can supplement or replace waterside system 120 inHVAC system 100 or can be implemented separate from HVAC system 100.When implemented in HVAC system 100, waterside system 200 can include asubset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller102, pumps, valves, etc.) and can operate to supply a heated or chilledfluid to AHU 106. The HVAC devices of waterside system 200 can belocated within building 10 (e.g., as components of waterside system 120)or at an offsite location such as a central plant.

In FIG. 2, waterside system 200 is shown as a central plant having aplurality of subplants 202-212. Subplants 202-212 are shown to include aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve the thermal energy loads(e.g., hot water, cold water, heating, cooling, etc.) of a building orcampus. For example, heater subplant 202 can be configured to heat waterin a hot water loop 214 that circulates the hot water between heatersubplant 202 and building 10. Chiller subplant 206 can be configured tochill water in a cold water loop 216 that circulates the cold waterbetween chiller subplant 206 building 10. Heat recovery chiller subplant204 can be configured to transfer heat from cold water loop 216 to hotwater loop 214 to provide additional heating for the hot water andadditional cooling for the cold water. Condenser water loop 218 canabsorb heat from the cold water in chiller subplant 206 and reject theabsorbed heat in cooling tower subplant 208 or transfer the absorbedheat to hot water loop 214. Hot TES subplant 210 and cold TES subplant212 can store hot and cold thermal energy, respectively, for subsequentuse.

Hot water loop 214 and cold water loop 216 can deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve the thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) can be used inplace of or in addition to water to serve the thermal energy loads. Inother embodiments, subplants 202-212 can provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 200are within the teachings of the present invention.

Each of subplants 202-212 can include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 can alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 can also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in waterside system 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines inwaterside system 200 include an isolation valve associated therewith.Isolation valves can be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 200. In various embodiments, waterside system 200 can includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 200 and the types of loadsserved by waterside system 200.

Referring now to FIG. 3, a block diagram of an airside system 300 isshown, according to an exemplary embodiment. In various embodiments,airside system 300 can supplement or replace airside system 130 in HVACsystem 100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 can include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and can be located in or aroundbuilding 10. Airside system 300 can operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 200.

In FIG. 3, airside system 300 is shown to include an economizer-type airhandling unit (AHU) 302. Economizer-type AHUs vary the amount of outsideair and return air used by the air handling unit for heating or cooling.For example, AHU 302 can receive return air 304 from building zone 306via return air duct 308 and can deliver supply air 310 to building zone306 via supply air duct 312. In some embodiments, AHU 302 is a rooftopunit located on the roof of building 10 (e.g., AHU 106 as shown inFIG. 1) or otherwise positioned to receive both return air 304 andoutside air 314. AHU 302 can be configured to operate exhaust air damper316, mixing damper 318, and outside air damper 320 to control an amountof outside air 314 and return air 304 that combine to form supply air310. Any return air 304 that does not pass through mixing damper 318 canbe exhausted from AHU 302 through exhaust damper 316 as exhaust air 322.

Each of dampers 316-320 can be operated by an actuator. For example,exhaust air damper 316 can be operated by actuator 324, mixing damper318 can be operated by actuator 326, and outside air damper 320 can beoperated by actuator 328. Actuators 324-328 can communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 canreceive control signals from AHU controller 330 and can provide feedbacksignals to AHU controller 330. Feedback signals can include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat can be collected, stored, or used by actuators 324-328. AHUcontroller 330 can be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3, AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 can be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 can communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 can receive a chilled fluid from waterside system 200(e.g., from cold water loop 216) via piping 342 and can return thechilled fluid to waterside system 200 via piping 344. Valve 346 can bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 can receive a heated fluid from waterside system 200(e.g., from hot water loop 214) via piping 348 and can return the heatedfluid to waterside system 200 via piping 350. Valve 352 can bepositioned along piping 348 or piping 350 to control a flow rate of theheated fluid through heating coil 336. In some embodiments, heating coil336 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, by BMScontroller 366, etc.) to modulate an amount of heating applied to supplyair 310.

Each of valves 346 and 352 can be controlled by an actuator. Forexample, valve 346 can be controlled by actuator 354 and valve 352 canbe controlled by actuator 356. Actuators 354-356 can communicate withAHU controller 330 via communications links 358-360. Actuators 354-356can receive control signals from AHU controller 330 and can providefeedback signals to controller 330. In some embodiments, AHU controller330 receives a measurement of the supply air temperature from atemperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 can also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a setpoint temperature for supplyair 310 or to maintain the temperature of supply air 310 within asetpoint temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by coolingcoil 334 or heating coil 336 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU controller 330can control the temperature of supply air 310 and/or building zone 306by activating or deactivating coils 334-336, adjusting a speed of fan338, or a combination of both.

Still referring to FIG. 3, airside system 300 is shown to include abuilding management system (BMS) controller 366 and a client device 368.BMS controller 366 can include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 300, waterside system200, HVAC system 100, and/or other controllable systems that servebuilding 10. BMS controller 366 can communicate with multiple downstreambuilding systems or subsystems (e.g., HVAC system 100, a securitysystem, a lighting system, waterside system 200, etc.) via acommunications link 370 according to like or disparate protocols (e.g.,LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMScontroller 366 can be separate (as shown in FIG. 3) or integrated. In anintegrated implementation, AHU controller 330 can be a software moduleconfigured for execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information from BMScontroller 366 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 366 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 330 can provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 can include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 can be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 can be a stationary terminal or amobile device. For example, client device 368 can be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 can communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Referring now to FIG. 4, a block diagram of a building management system(BMS) 400 is shown, according to an exemplary embodiment. BMS 400 can beimplemented in building 10 to automatically monitor and control variousbuilding functions. BMS 400 is shown to include BMS controller 366 and aplurality of building subsystems 428. Building subsystems 428 are shownto include a building electrical subsystem 434, an informationcommunication technology (ICT) subsystem 436, a security subsystem 438,a HVAC subsystem 440, a lighting subsystem 442, a lift/escalatorssubsystem 432, and a fire safety subsystem 430. In various embodiments,building subsystems 428 can include fewer, additional, or alternativesubsystems. For example, building subsystems 428 can also oralternatively include a refrigeration subsystem, an advertising orsignage subsystem, a cooking subsystem, a vending subsystem, a printeror copy service subsystem, or any other type of building subsystem thatuses controllable equipment and/or sensors to monitor or controlbuilding 10. In some embodiments, building subsystems 428 includewaterside system 200 and/or airside system 300, as described withreference to FIGS. 2-3.

Each of building subsystems 428 can include any number of devices,controllers, and connections for completing its individual functions andcontrol activities. HVAC subsystem 440 can include many of the samecomponents as HVAC system 100, as described with reference to FIGS. 1-3.For example, HVAC subsystem 440 can include a chiller, a boiler, anynumber of air handling units, economizers, field controllers,supervisory controllers, actuators, temperature sensors, and otherdevices for controlling the temperature, humidity, airflow, or othervariable conditions within building 10. Lighting subsystem 442 caninclude any number of light fixtures, ballasts, lighting sensors,dimmers, or other devices configured to controllably adjust the amountof light provided to a building space. Security subsystem 438 caninclude occupancy sensors, video surveillance cameras, digital videorecorders, video processing servers, intrusion detection devices, accesscontrol devices and servers, or other security-related devices.

Still referring to FIG. 4, BMS controller 366 is shown to include acommunications interface 407 and a BMS interface 409. Interface 407 canfacilitate communications between BMS controller 366 and externalapplications (e.g., monitoring and reporting applications 422,enterprise control applications 426, remote systems and applications444, applications residing on client devices 448, etc.) for allowinguser control, monitoring, and adjustment to BMS controller 366 and/orsubsystems 428. Interface 407 can also facilitate communications betweenBMS controller 366 and client devices 448. BMS interface 409 canfacilitate communications between BMS controller 366 and buildingsubsystems 428 (e.g., HVAC, lighting security, lifts, powerdistribution, business, etc.).

Interfaces 407, 409 can be or include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith building subsystems 428 or other external systems or devices. Invarious embodiments, communications via interfaces 407, 409 can bedirect (e.g., local wired or wireless communications) or via acommunications network 446 (e.g., a WAN, the Internet, a cellularnetwork, etc.). For example, interfaces 407, 409 can include an Ethernetcard and port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 407, 409can include a WiFi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces407, 409 can include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 407 is a powerline communications interface and BMS interface 409 is an Ethernetinterface. In other embodiments, both communications interface 407 andBMS interface 409 are Ethernet interfaces or are the same Ethernetinterface.

Still referring to FIG. 4, BMS controller 366 is shown to include aprocessing circuit 404 including a processor 406 and memory 408.Processing circuit 404 can be communicably connected to BMS interface409 and/or communications interface 407 such that processing circuit 404and the various components thereof can send and receive data viainterfaces 407, 409. Processor 406 can be implemented as a generalpurpose processor, an application specific integrated circuit (ASIC),one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents.

Memory 408 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 408 can be or include volatile memory ornon-volatile memory. Memory 408 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to anexemplary embodiment, memory 408 is communicably connected to processor406 via processing circuit 404 and includes computer code for executing(e.g., by processing circuit 404 and/or processor 406) one or moreprocesses described herein.

In some embodiments, BMS controller 366 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 366 can be distributed across multipleservers or computers (e.g., that can exist in distributed locations).Further, while FIG. 4 shows applications 422 and 426 as existing outsideof BMS controller 366, in some embodiments, applications 422 and 426 canbe hosted within BMS controller 366 (e.g., within memory 408).

Still referring to FIG. 4, memory 408 is shown to include an enterpriseintegration layer 410, an automated measurement and validation (AM&V)layer 412, a demand response (DR) layer 414, a fault detection anddiagnostics (FDD) layer 416, an integrated control layer 418, and abuilding subsystem integration later 420. Layers 410-420 can beconfigured to receive inputs from building subsystems 428 and other datasources, determine optimal control actions for building subsystems 428based on the inputs, generate control signals based on the optimalcontrol actions, and provide the generated control signals to buildingsubsystems 428. The following paragraphs describe some of the generalfunctions performed by each of layers 410-420 in BMS 400.

Enterprise integration layer 410 can be configured to serve clients orlocal applications with information and services to support a variety ofenterprise-level applications. For example, enterprise controlapplications 426 can be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 426 can also oralternatively be configured to provide configuration GUIs forconfiguring BMS controller 366. In yet other embodiments, enterprisecontrol applications 426 can work with layers 410-420 to optimizebuilding performance (e.g., efficiency, energy use, comfort, or safety)based on inputs received at interface 407 and/or BMS interface 409.

Building subsystem integration layer 420 can be configured to managecommunications between BMS controller 366 and building subsystems 428.For example, building subsystem integration layer 420 can receive sensordata and input signals from building subsystems 428 and provide outputdata and control signals to building subsystems 428. Building subsystemintegration layer 420 can also be configured to manage communicationsbetween building subsystems 428. Building subsystem integration layer420 translate communications (e.g., sensor data, input signals, outputsignals, etc.) across a plurality of multi-vendor/multi-protocolsystems.

Demand response layer 414 can be configured to optimize resource usage(e.g., electricity use, natural gas use, water use, etc.) and/or themonetary cost of such resource usage in response to satisfy the demandof building 10. The optimization can be based on time-of-use prices,curtailment signals, energy availability, or other data received fromutility providers, distributed energy generation systems 424, fromenergy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or fromother sources. Demand response layer 414 can receive inputs from otherlayers of BMS controller 366 (e.g., building subsystem integration layer420, integrated control layer 418, etc.). The inputs received from otherlayers can include environmental or sensor inputs such as temperature,carbon dioxide levels, relative humidity levels, air quality sensoroutputs, occupancy sensor outputs, room schedules, and the like. Theinputs can also include inputs such as electrical use (e.g., expressedin kWh), thermal load measurements, pricing information, projectedpricing, smoothed pricing, curtailment signals from utilities, and thelike.

According to an exemplary embodiment, demand response layer 414 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms inintegrated control layer 418, changing control strategies, changingsetpoints, or activating/deactivating building equipment or subsystemsin a controlled manner. Demand response layer 414 can also includecontrol logic configured to determine when to utilize stored energy. Forexample, demand response layer 414 can determine to begin using energyfrom energy storage 427 just prior to the beginning of a peak use hour.

In some embodiments, demand response layer 414 includes a control moduleconfigured to actively initiate control actions (e.g., automaticallychanging setpoints) which minimize energy costs based on one or moreinputs representative of or based on demand (e.g., price, a curtailmentsignal, a demand level, etc.). In some embodiments, demand responselayer 414 uses equipment models to determine an optimal set of controlactions. The equipment models can include, for example, thermodynamicmodels describing the inputs, outputs, and/or functions performed byvarious sets of building equipment. Equipment models can representcollections of building equipment (e.g., subplants, chiller arrays,etc.) or individual devices (e.g., individual chillers, heaters, pumps,etc.).

Demand response layer 414 can further include or draw upon one or moredemand response policy definitions (e.g., databases, XML files, etc.).The policy definitions can be edited or adjusted by a user (e.g., via agraphical user interface) so that the control actions initiated inresponse to demand inputs can be tailored for the user's application,desired comfort level, particular building equipment, or based on otherconcerns. For example, the demand response policy definitions canspecify which equipment can be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.).

Integrated control layer 418 can be configured to use the data input oroutput of building subsystem integration layer 420 and/or demandresponse later 414 to make control decisions. Due to the subsystemintegration provided by building subsystem integration layer 420,integrated control layer 418 can integrate control activities of thesubsystems 428 such that the subsystems 428 behave as a singleintegrated supersystem. In an exemplary embodiment, integrated controllayer 418 includes control logic that uses inputs and outputs from aplurality of building subsystems to provide greater comfort and energysavings relative to the comfort and energy savings that separatesubsystems could provide alone. For example, integrated control layer418 can be configured to use an input from a first subsystem to make anenergy-saving control decision for a second subsystem. Results of thesedecisions can be communicated back to building subsystem integrationlayer 420.

Integrated control layer 418 is shown to be logically below demandresponse layer 414. Integrated control layer 418 can be configured toenhance the effectiveness of demand response layer 414 by enablingbuilding subsystems 428 and their respective control loops to becontrolled in coordination with demand response layer 414. Thisconfiguration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, integratedcontrol layer 418 can be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller.

Integrated control layer 418 can be configured to provide feedback todemand response layer 414 so that demand response layer 414 checks thatconstraints (e.g., temperature, lighting levels, etc.) are properlymaintained even while demanded load shedding is in progress. Theconstraints can also include setpoint or sensed boundaries relating tosafety, equipment operating limits and performance, comfort, fire codes,electrical codes, energy codes, and the like. Integrated control layer418 is also logically below fault detection and diagnostics layer 416and automated measurement and validation layer 412. Integrated controllayer 418 can be configured to provide calculated inputs (e.g.,aggregations) to these higher levels based on outputs from more than onebuilding subsystem.

Automated measurement and validation (AM&V) layer 412 can be configuredto verify that control strategies commanded by integrated control layer418 or demand response layer 414 are working properly (e.g., using dataaggregated by AM&V layer 412, integrated control layer 418, buildingsubsystem integration layer 420, FDD layer 416, or otherwise). Thecalculations made by AM&V layer 412 can be based on building systemenergy models and/or equipment models for individual BMS devices orsubsystems. For example, AM&V layer 412 can compare a model-predictedoutput with an actual output from building subsystems 428 to determinean accuracy of the model.

Fault detection and diagnostics (FDD) layer 416 can be configured toprovide on-going fault detection for building subsystems 428, buildingsubsystem devices (i.e., building equipment), and control algorithmsused by demand response layer 414 and integrated control layer 418. FDDlayer 416 can receive data inputs from integrated control layer 418,directly from one or more building subsystems or devices, or fromanother data source. FDD layer 416 can automatically diagnose andrespond to detected faults. The responses to detected or diagnosedfaults can include providing an alert message to a user, a maintenancescheduling system, or a control algorithm configured to attempt torepair the fault or to work-around the fault.

FDD layer 416 can be configured to output a specific identification ofthe faulty component or cause of the fault (e.g., loose damper linkage)using detailed subsystem inputs available at building subsystemintegration layer 420. In other exemplary embodiments, FDD layer 416 isconfigured to provide “fault” events to integrated control layer 418which executes control strategies and policies in response to thereceived fault events. According to an exemplary embodiment, FDD layer416 (or a policy executed by an integrated control engine or businessrules engine) can shut-down systems or direct control activities aroundfaulty devices or systems to reduce energy waste, extend equipment life,or assure proper control response.

FDD layer 416 can be configured to store or access a variety ofdifferent system data stores (or data points for live data). FDD layer416 can use some content of the data stores to identify faults at theequipment level (e.g., specific chiller, specific AHU, specific terminalunit, etc.) and other content to identify faults at component orsubsystem levels. For example, building subsystems 428 can generatetemporal (i.e., time-series) data indicating the performance of BMS 400and the various components thereof. The data generated by buildingsubsystems 428 can include measured or calculated values that exhibitstatistical characteristics and provide information about how thecorresponding system or process (e.g., a temperature control process, aflow control process, etc.) is performing in terms of error from itssetpoint. These processes can be examined by FDD layer 416 to exposewhen the system begins to degrade in performance and alert a user torepair the fault before it becomes more severe.

Building Management System with Data Platform Services

Referring now to FIG. 5, a block diagram of another building managementsystem (BMS) 500 is shown, according to some embodiments. BMS 500 isconfigured to collect data samples from building subsystems 428 andgenerate raw timeseries data from the data samples. BMS 500 can processthe raw timeseries data using a variety of data platform services 520 togenerate optimized timeseries data (e.g., data rollups). The optimizedtimeseries data can be provided to various applications 530 and/orstored in local storage 514 or hosted storage 516. In some embodiments,BMS 500 separates data collection; data storage, retrieval, andanalysis; and data visualization into three different layers. Thisallows BMS 500 to support a variety of applications 530 that use theoptimized timeseries data and allows new applications 530 to reuse theexisting infrastructure provided by data platform services 520.

Before discussing BMS 500 in greater detail, it should be noted that thecomponents of BMS 500 can be integrated within a single device (e.g., asupervisory controller, a BMS controller, etc.) or distributed acrossmultiple separate systems or devices. For example, the components of BMS500 can be implemented as part of a METASYS® brand building automationsystem, as sold by Johnson Controls Inc. In other embodiments, some orall of the components of BMS 500 can be implemented as part of acloud-based computing system configured to receive and process data fromone or more building management systems. In other embodiments, some orall of the components of BMS 500 can be components of a subsystem levelcontroller (e.g., a HVAC controller), a subplant controller, a devicecontroller (e.g., AHU controller 330, a chiller controller, etc.), afield controller, a computer workstation, a client device, or any othersystem or device that receives and processes data from buildingequipment.

BMS 500 can include many of the same components as BMS 400, as describedwith reference to FIG. 4. For example, BMS 500 is shown to include a BMSinterface 502 and a communications interface 504. Interfaces 502-504 caninclude wired or wireless communications interfaces (e.g., jacks,antennas, transmitters, receivers, transceivers, wire terminals, etc.)for conducting data communications with building subsystems 428 or otherexternal systems or devices. Communications conducted via interfaces502-504 can be direct (e.g., local wired or wireless communications) orvia a communications network 446 (e.g., a WAN, the Internet, a cellularnetwork, etc.).

Communications interface 504 can facilitate communications between BMS500 and external applications (e.g., remote systems and applications444) for allowing user control, monitoring, and adjustment to BMS 500.Communications interface 504 can also facilitate communications betweenBMS 500 and client devices 448. BMS interface 502 can facilitatecommunications between BMS 500 and building subsystems 428. BMS 500 canbe configured to communicate with building subsystems 428 using any of avariety of building automation systems protocols (e.g., BACnet, Modbus,ADX, etc.). In some embodiments, BMS 500 receives data samples frombuilding subsystems 428 and provides control signals to buildingsubsystems 428 via BMS interface 502.

Building subsystems 428 can include building electrical subsystem 434,information communication technology (ICT) subsystem 436, securitysubsystem 438, HVAC subsystem 440, lighting subsystem 442,lift/escalators subsystem 432, and/or fire safety subsystem 430, asdescribed with reference to FIG. 4. In various embodiments, buildingsubsystems 428 can include fewer, additional, or alternative subsystems.For example, building subsystems 428 can also or alternatively include arefrigeration subsystem, an advertising or signage subsystem, a cookingsubsystem, a vending subsystem, a printer or copy service subsystem, orany other type of building subsystem that uses controllable equipmentand/or sensors to monitor or control building 10. In some embodiments,building subsystems 428 include waterside system 200 and/or airsidesystem 300, as described with reference to FIGS. 2-3. Each of buildingsubsystems 428 can include any number of devices, controllers, andconnections for completing its individual functions and controlactivities. Building subsystems 428 can include building equipment(e.g., sensors, air handling units, chillers, pumps, valves, etc.)configured to monitor and control a building condition such astemperature, humidity, airflow, etc.

Still referring to FIG. 5, BMS 500 is shown to include a processingcircuit 506 including a processor 508 and memory 510. Processor 508 canbe a general purpose or specific purpose processor, an applicationspecific integrated circuit (ASIC), one or more field programmable gatearrays (FPGAs), a group of processing components, or other suitableprocessing components. Processor 508 is configured to execute computercode or instructions stored in memory 510 or received from othercomputer readable media (e.g., CDROM, network storage, a remote server,etc.).

Memory 510 can include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 510 can include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory510 can include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 510 can be communicably connected toprocessor 508 via processing circuit 506 and can include computer codefor executing (e.g., by processor 508) one or more processes describedherein. When processor 508 executes instructions stored in memory 510,processor 508 generally configures processing circuit 506 to completesuch activities.

Still referring to FIG. 5, BMS 500 is shown to include a data collector512. Data collector 512 is shown receiving data samples from buildingsubsystems 428 via BMS interface 502. In some embodiments, the datasamples include data values for various data points. The data values canbe measured or calculated values, depending on the type of data point.For example, a data point received from a temperature sensor can includea measured data value indicating a temperature measured by thetemperature sensor. A data point received from a chiller controller caninclude a calculated data value indicating a calculated efficiency ofthe chiller. Data collector 512 can receive data samples from multipledifferent devices within building subsystems 428.

The data samples can include one or more attributes that describe orcharacterize the corresponding data points. For example, the datasamples can include a name attribute defining a point name or ID (e.g.,“B1F4R2.T-Z”), a device attribute indicating a type of device from whichthe data samples is received (e.g., temperature sensor, humidity sensor,chiller, etc.), a unit attribute defining a unit of measure associatedwith the data value (e.g., ° F., ° C., kPA, etc.), and/or any otherattribute that describes the corresponding data point or providescontextual information regarding the data point. The types of attributesincluded in each data point can depend on the communications protocolused to send the data samples to BMS 500. For example, data samplesreceived via the ADX protocol or BACnet protocol can include a varietyof descriptive attributes along with the data value, whereas datasamples received via the Modbus protocol may include a lesser number ofattributes (e.g., only the data value without any correspondingattributes).

In some embodiments, each data sample is received with a timestampindicating a time at which the corresponding data value was measured orcalculated. In other embodiments, data collector 512 adds timestamps tothe data samples based on the times at which the data samples arereceived. Data collector 512 can generate raw timeseries data for eachof the data points for which data samples are received. Each timeseriescan include a series of data values for the same data point and atimestamp for each of the data values. For example, a timeseries for adata point provided by a temperature sensor can include a series oftemperature values measured by the temperature sensor and thecorresponding times at which the temperature values were measured.

Data collector 512 can add timestamps to the data samples or modifyexisting timestamps such that each data sample includes a localtimestamp. Each local timestamp indicates the local time at which thecorresponding data sample was measured or collected and can include anoffset relative to universal time. The local timestamp indicates thelocal time at the location the data point was measured at the time ofmeasurement. The offset indicates the difference between the local timeand a universal time (e.g., the time at the international date line).For example, a data sample collected in a time zone that is six hoursbehind universal time can include a local timestamp (e.g.,Timestamp=2016-03-18T14:10:02) and an offset indicating that the localtimestamp is six hours behind universal time (e.g., Offset=−6:00). Theoffset can be adjusted (e.g., +1:00 or −1:00) depending on whether thetime zone is in daylight savings time when the data sample is measuredor collected.

The combination of the local timestamp and the offset provides a uniquetimestamp across daylight saving time boundaries. This allows anapplication using the timeseries data to display the timeseries data inlocal time without first converting from universal time. The combinationof the local timestamp and the offset also provides enough informationto convert the local timestamp to universal time without needing to lookup a schedule of when daylight savings time occurs. For example, theoffset can be subtracted from the local timestamp to generate auniversal time value that corresponds to the local timestamp withoutreferencing an external database and without requiring any otherinformation.

In some embodiments, data collector 512 organizes the raw timeseriesdata. Data collector 512 can identify a system or device associated witheach of the data points. For example, data collector 512 can associate adata point with a temperature sensor, an air handler, a chiller, or anyother type of system or device. In various embodiments, data collectoruses the name of the data point, a range of values of the data point,statistical characteristics of the data point, or other attributes ofthe data point to identify a particular system or device associated withthe data point. Data collector 512 can then determine how that system ordevice relates to the other systems or devices in the building site. Forexample, data collector 512 can determine that the identified system ordevice is part of a larger system (e.g., a HVAC system) or serves aparticular space (e.g., a particular building, a room or zone of thebuilding, etc.). In some embodiments, data collector 512 uses or createsan entity graph when organizing the timeseries data. An example of suchan entity graph is described in greater detail with reference to FIG.10A.

Data collector 512 can provide the raw timeseries data to data platformservices 520 and/or store the raw timeseries data in local storage 514or hosted storage 516. As shown in FIG. 5, local storage 514 can be datastorage internal to BMS 500 (e.g., within memory 510) or other on-sitedata storage local to the building site at which the data samples arecollected. Hosted storage 516 can include a remote database, cloud-baseddata hosting, or other remote data storage. For example, hosted storage516 can include remote data storage located off-site relative to thebuilding site at which the data samples are collected.

Still referring to FIG. 5, BMS 500 is shown to include data platformservices 520. Data platform services 520 can receive the raw timeseriesdata from data collector 512 and/or retrieve the raw timeseries datafrom local storage 514 or hosted storage 516. Data platform services 520can include a variety of services configured to analyze and process theraw timeseries data. For example, data platform services 520 are shownto include a security service 522, an analytics service 524, an entityservice 526, and a timeseries service 528. Security service 522 canassign security attributes to the raw timeseries data to ensure that thetimeseries data are only accessible to authorized individuals, systems,or applications. Entity service 524 can assign entity information to thetimeseries data to associate data points with a particular system,device, or space. Timeseries service 528 and analytics service 524 cangenerate new optimized timeseries from the raw timeseries data.

In some embodiments, timeseries service 528 aggregates predefinedintervals of the raw timeseries data (e.g., quarter-hourly intervals,hourly intervals, daily intervals, monthly intervals, etc.) to generatenew optimized timeseries of the aggregated values. These optimizedtimeseries can be referred to as “data rollups” since they are condensedversions of the raw timeseries data. The data rollups generated bytimeseries service 528 provide an efficient mechanism for applications530 to query the timeseries data. For example, applications 530 canconstruct visualizations of the timeseries data (e.g., charts, graphs,etc.) using the pre-aggregated data rollups instead of the rawtimeseries data. This allows applications 530 to simply retrieve andpresent the pre-aggregated data rollups without requiring applications530 to perform an aggregation in response to the query. Since the datarollups are pre-aggregated, applications 530 can present the datarollups quickly and efficiently without requiring additional processingat query time to generate aggregated timeseries values.

In some embodiments, timeseries service 528 calculates virtual pointsbased on the raw timeseries data and/or the optimized timeseries data.Virtual points can be calculated by applying any of a variety ofmathematical operations (e.g., addition, subtraction, multiplication,division, etc.) or functions (e.g., average value, maximum value,minimum value, thermodynamic functions, linear functions, nonlinearfunctions, etc.) to the actual data points represented by the timeseriesdata. For example, timeseries service 528 can calculate a virtual datapoint (pointID₃) by adding two or more actual data points (pointID₁ andpointID₂) (e.g., pointID₃=pointID₁+pointID₂). As another example,timeseries service 528 can calculate an enthalpy data point (pointID₄)based on a measured temperature data point (pointID₅) and a measuredpressure data point (pointID₆) (e.g., pointID₄=enthalpy(pointID₅,pointID₆)). The virtual data points can be stored as optimizedtimeseries data.

Applications 530 can access and use the virtual data points in the samemanner as the actual data points. Applications 530 do not need to knowwhether a data point is an actual data point or a virtual data pointsince both types of data points can be stored as optimized timeseriesdata and can be handled in the same manner by applications 530. In someembodiments, the optimized timeseries data are stored with attributesdesignating each data point as either a virtual data point or an actualdata point. Such attributes allow applications 530 to identify whether agiven timeseries represents a virtual data point or an actual datapoint, even though both types of data points can be handled in the samemanner by applications 530.

In some embodiments, analytics service 524 analyzes the raw timeseriesdata and/or the optimized timeseries data to detect faults. Analyticsservice 524 can apply a set of fault detection rules to the timeseriesdata to determine whether a fault is detected at each interval of thetimeseries. Fault detections can be stored as optimized timeseries data.For example, analytics service 524 can generate a new timeseries withdata values that indicate whether a fault was detected at each intervalof the timeseries. The time series of fault detections can be storedalong with the raw timeseries data and/or optimized timeseries data inlocal storage 514 or hosted storage 516. These and other features ofanalytics service 524 and timeseries service 528 are described ingreater detail with reference to FIG. 6.

Still referring to FIG. 5, BMS 500 is shown to include severalapplications 530 including an energy management application 532,monitoring and reporting applications 534, and enterprise controlapplications 536. Although only a few applications 530 are shown, it iscontemplated that applications 530 can include any of a variety ofapplications configured to use the optimized timeseries data generatedby data platform services 520. In some embodiments, applications 530exist as a separate layer of BMS 500 (i.e., separate from data platformservices 520 and data collector 512). This allows applications 530 to beisolated from the details of how the optimized timeseries data aregenerated. In other embodiments, applications 530 can exist as remoteapplications that run on remote systems or devices (e.g., remote systemsand applications 544, client devices 448).

Applications 530 can use the optimized timeseries data to perform avariety data visualization, monitoring, and/or control activities. Forexample, energy management application 532 and monitoring and reportingapplication 534 can use the optimized timeseries data to generate userinterfaces (e.g., charts, graphs, etc.) that present the optimizedtimeseries data to a user. In some embodiments, the user interfacespresent the raw timeseries data and the optimized data rollups in asingle chart or graph. For example, a dropdown selector can be providedto allow a user to select the raw timeseries data or any of the datarollups for a given data point. Several examples of user interfaces thatcan be generated based on the optimized timeseries data are shown inFIGS. 15 and 17.

Enterprise control application 536 can use the optimized timeseries datato perform various control activities. For example, enterprise controlapplication 536 can use the optimized timeseries data as input to acontrol algorithm (e.g., a state-based algorithm, an extremum seekingcontrol (ESC) algorithm, a proportional-integral (PI) control algorithm,a proportional-integral-derivative (PID) control algorithm, a modelpredictive control (MPC) algorithm, a feedback control algorithm, etc.)to generate control signals for building subsystems 428. In someembodiments, building subsystems 428 use the control signals to operatebuilding equipment. Operating the building equipment can affect themeasured or calculated values of the data samples provided to BMS 500.Accordingly, enterprise control application 536 can use the optimizedtimeseries data as feedback to control the systems and devices ofbuilding subsystems 428.

Still referring to FIG. 5, BMS 500 is shown to include a dashboardlayout generator 518. Dashboard layout generator 518 is configured togenerate a layout for a user interface (i.e., a dashboard) visualizingthe timeseries data. In some embodiments, the dashboard layout is notitself a user interface, but rather a description which can be used byapplications 530 to generate the user interface. In some embodiments,the dashboard layout is a schema that defines the relative locations ofvarious widgets (e.g., charts, graphs, etc.) which can be rendered anddisplayed as part of the user interface. The dashboard layout can beread by a variety of different frameworks and can be used by a varietyof different rendering engines (e.g., a web browser, a pdf engine, etc.)or applications 530 to generate the user interface.

In some embodiments, the dashboard layout defines a grid having one ormore rows and one or more columns located within each row. The dashboardlayout can define the location of each widget at a particular locationwithin the grid. The dashboard layout can define an array of objects(e.g., JSON objects), each of which is itself an array. In someembodiments, the dashboard layout defines attributes or properties ofeach widget. For example, the dashboard layout can define the type ofwidget (e.g., graph, plain text, image, etc.). If the widget is a graph,the dashboard layout can define additional properties such as graphtitle, x-axis title, y-axis title, and the timeseries data used in thegraph. Dashboard layout generator 518 and the dashboard layouts aredescribed in greater detail with reference to FIGS. 12-17.

Timeseries and Analytics Data Platform Services

Referring now to FIG. 6, a block diagram illustrating timeseries service528 and analytics service 524 in greater detail is shown, according tosome embodiments. Timeseries service 528 is shown to include atimeseries web service 602, a job manager 604, and a timeseries storageinterface 616. Timeseries web service 602 is configured to interact withweb-based applications to send and/or receive timeseries data. In someembodiments, timeseries web service 602 provides timeseries data toweb-based applications. For example, if one or more of applications 530are web-based applications, timeseries web service 602 can provideoptimized timeseries data and raw timeseries data to the web-basedapplications. In some embodiments, timeseries web service 602 receivesraw timeseries data from a web-based data collector. For example, ifdata collector 512 is a web-based application, timeseries web service602 can receive data samples or raw timeseries data from data collector512.

Timeseries storage interface 616 is configured to interact with localstorage 514 and/or hosted storage 516. For example, timeseries storageinterface 616 can retrieve raw timeseries data from a local timeseriesdatabase 628 within local storage 514 or from a hosted timeseriesdatabase 636 within hosted storage 516. Timeseries storage interface 616can also store optimized timeseries data in local timeseries database628 or hosted timeseries database 636. In some embodiments, timeseriesstorage interface 616 is configured to retrieve jobs from a local jobqueue 630 within local storage 514 or from a hosted job queue 638 withinhosted storage 516. Timeseries storage interface 616 can also store jobswithin local job queue 630 or hosted job queue 638. Jobs can be createdand/or processed by job manager 604 to generate optimized timeseriesdata from the raw timeseries data.

Still referring to FIG. 6, job manager 604 is shown to include a sampleaggregator 608. Sample aggregator 608 is configured to generateoptimized data rollups from the raw timeseries data. For each datapoint, sample aggregator 608 can aggregate a set of data values havingtimestamps within a predetermined time interval (e.g., a quarter-hour,an hour, a day, etc.) to generate an aggregate data value for thepredetermined time interval. For example, the raw timeseries data for aparticular data point may have a relatively short interval (e.g., oneminute) between consecutive samples of the data point. Sample aggregator608 can generate a data rollup from the raw timeseries data byaggregating all of the samples of the data point having timestampswithin a relatively longer interval (e.g., a quarter-hour) into a singleaggregated value that represents the longer interval.

For some types of timeseries, sample aggregator 608 performs theaggregation by averaging all of the samples of the data point havingtimestamps within the longer interval. Aggregation by averaging can beused to calculate aggregate values for timeseries of non-cumulativevariables such as measured value. For other types of timeseries, sampleaggregator 608 performs the aggregation by summing all of the samples ofthe data point having timestamps within the longer interval. Aggregationby summation can be used to calculate aggregate values for timeseries ofcumulative variables such as the number of faults detected since theprevious sample.

Referring now to FIGS. 7A-7B, a block diagram 700 and a data table 750illustrating an aggregation technique which can be used by sampleaggregator 608 is shown, according to some embodiments. In FIG. 7A, adata point 702 is shown. Data point 702 is an example of a measured datapoint for which timeseries values can be obtained. For example, datapoint 702 is shown as an outdoor air temperature point and has valueswhich can be measured by a temperature sensor. Although a specific typeof data point 702 is shown in FIG. 7A, it should be understood that datapoint 702 can be any type of measured or calculated data point.Timeseries values of data point 702 can be collected by data collector512 and assembled into a raw data timeseries 704.

As shown in FIG. 7B, the raw data timeseries 704 includes a timeseriesof data samples, each of which is shown as a separate row in data table750. Each sample of raw data timeseries 704 is shown to include atimestamp and a data value. The timestamps of raw data timeseries 704are ten minutes and one second apart, indicating that the samplinginterval of raw data timeseries 704 is ten minutes and one second. Forexample, the timestamp of the first data sample is shown as2015-12-31T23:10:00 indicating that the first data sample of raw datatimeseries 704 was collected at 11:10:00 PM on Dec. 31, 2015. Thetimestamp of the second data sample is shown as 2015-12-31T23:20:01indicating that the second data sample of raw data timeseries 704 wascollected at 11:20:01 PM on Dec. 31, 2015. In some embodiments, thetimestamps of raw data timeseries 704 are stored along with an offsetrelative to universal time, as previously described. The values of rawdata timeseries 704 start at a value of 10 and increase by 10 with eachsample. For example, the value of the second sample of raw datatimeseries 704 is 20, the value of the third sample of raw datatimeseries 704 is 30, etc.

In FIG. 7A, several data rollup timeseries 706-714 are shown. Datarollup timeseries 706-714 can be generated by sample aggregator 608 andstored as optimized timeseries data. The data rollup timeseries 706-714include an average quarter-hour timeseries 706, an average hourlytimeseries 708, an average daily timeseries 710, an average monthlytimeseries 712, and an average yearly timeseries 714. Each of the datarollup timeseries 706-714 is dependent upon a parent timeseries. In someembodiments, the parent timeseries for each of the data rolluptimeseries 706-714 is the timeseries with the next shortest durationbetween consecutive timeseries values. For example, the parenttimeseries for average quarter-hour timeseries 706 is raw datatimeseries 704. Similarly, the parent timeseries for average hourlytimeseries 708 is average quarter-hour timeseries 706; the parenttimeseries for average daily timeseries 710 is average hourly timeseries708; the parent timeseries for average monthly timeseries 712 is averagedaily timeseries 710; and the parent timeseries for average yearlytimeseries 714 is average monthly timeseries 712.

Sample aggregator 608 can generate each of the data rollup timeseries706-714 from the timeseries values of the corresponding parenttimeseries. For example, sample aggregator 608 can generate averagequarter-hour timeseries 706 by aggregating all of the samples of datapoint 702 in raw data timeseries 704 that have timestamps within eachquarter-hour. Similarly, sample aggregator 608 can generate averagehourly timeseries 708 by aggregating all of the timeseries values ofaverage quarter-hour timeseries 708 that have timestamps within eachhour. Sample aggregator 608 can generate average daily timeseries 710 byaggregating all of the time series values of average hourly timeseries708 that have timestamps within each day. Sample aggregator 608 cangenerate average monthly timeseries 712 by aggregating all of the timeseries values of average daily timeseries 710 that have timestampswithin each month. Sample aggregator 608 can generate average yearlytimeseries 714 by aggregating all of the time series values of averagemonthly timeseries 712 that have timestamps within each year.

In some embodiments, the timestamps for each sample in the data rolluptimeseries 706-714 are the beginnings of the aggregation interval usedto calculate the value of the sample. For example, the first data sampleof average quarter-hour timeseries 706 is shown to include the timestamp2015-12-31T23:00:00. This timestamp indicates that the first data sampleof average quarter-hour timeseries 706 corresponds to an aggregationinterval that begins at 11:00:00 PM on Dec. 31, 2015. Since only onedata sample of raw data timeseries 704 occurs during this interval, thevalue of the first data sample of average quarter-hour timeseries 706 isthe average of a single data value (i.e., average(10)=10). The same istrue for the second data sample of average quarter-hour timeseries 706(i.e., average(20)=20).

The third data sample of average quarter-hour timeseries 706 is shown toinclude the timestamp 2015-12-31T23:30:00. This timestamp indicates thatthe third data sample of average quarter-hour timeseries 706 correspondsto an aggregation interval that begins at 11:30:00 PM on Dec. 31, 2015.Since each aggregation interval of average quarter-hour timeseries 706is a quarter-hour in duration, the end of the aggregation interval is11:45:00 PM on Dec. 31, 2015. This aggregation interval includes twodata samples of raw data timeseries 704 (i.e., the third raw data samplehaving a value of 30 and the fourth raw data sample having a value of40). Sample aggregator 608 can calculate the value of the third sampleof average quarter-hour timeseries 706 by averaging the values of thethird raw data sample and the fourth raw data sample (i.e., average(30,40)=35). Accordingly, the third sample of average quarter-hourtimeseries 706 has a value of 35. Sample aggregator 608 can calculatethe remaining values of average quarter-hour timeseries 706 in a similarmanner.

Still referring to FIG. 7B, the first data sample of average hourlytimeseries 708 is shown to include the timestamp 2015-12-31T23:00:00.This timestamp indicates that the first data sample of average hourlytimeseries 708 corresponds to an aggregation interval that begins at11:00:00 PM on Dec. 31, 2015. Since each aggregation interval of averagehourly timeseries 708 is an hour in duration, the end of the aggregationinterval is 12:00:00 AM on Jan. 1, 2016. This aggregation intervalincludes the first four samples of average quarter-hour timeseries 706.Sample aggregator 608 can calculate the value of the first sample ofaverage hourly timeseries 708 by averaging the values of the first fourvalues of average quarter-hour timeseries 706 (i.e., average(10, 20, 35,50)=28.8). Accordingly, the first sample of average hourly timeseries708 has a value of 28.8. Sample aggregator 608 can calculate theremaining values of average hourly timeseries 708 in a similar manner.

The first data sample of average daily timeseries 710 is shown toinclude the timestamp 2015-12-31T00:00:00. This timestamp indicates thatthe first data sample of average daily timeseries 710 corresponds to anaggregation interval that begins at 12:00:00 AM on Dec. 31, 2015. Sinceeach aggregation interval of the average daily timeseries 710 is a dayin duration, the end of the aggregation interval is 12:00:00 AM on Jan.1, 2016. Only one data sample of average hourly timeseries 708 occursduring this interval. Accordingly, the value of the first data sample ofaverage daily timeseries 710 is the average of a single data value(i.e., average(28.8)=28.8). The same is true for the second data sampleof average daily timeseries 710 (i.e., average(87.5)=87.5).

In some embodiments, sample aggregator 608 stores each of the datarollup timeseries 706-714 in a single data table (e.g., data table 750)along with raw data timeseries 704. This allows applications 630 toretrieve all of the timeseries 704-714 quickly and efficiently byaccessing a single data table. In other embodiments, sample aggregator608 can store the various timeseries 704-714 in separate data tableswhich can be stored in the same data storage device (e.g., the samedatabase) or distributed across multiple data storage devices. In someembodiments, sample aggregator 608 stores data timeseries 704-714 in aformat other than a data table. For example, sample aggregator 608 canstore timeseries 704-714 as vectors, as a matrix, as a list, or usingany of a variety of other data storage formats.

In some embodiments, sample aggregator 608 automatically updates thedata rollup timeseries 706-714 each time a new raw data sample isreceived. Updating the data rollup timeseries 706-714 can includerecalculating the aggregated values based on the value and timestamp ofthe new raw data sample. When a new raw data sample is received, sampleaggregator 608 can determine whether the timestamp of the new raw datasample is within any of the aggregation intervals for the samples of thedata rollup timeseries 706-714. For example, if a new raw data sample isreceived with a timestamp of 2016-01-01T00:52:00, sample aggregator 608can determine that the new raw data sample occurs within the aggregationinterval beginning at timestamp 2016-01-01T00:45:00 for averagequarter-hour timeseries 706. Sample aggregator 608 can use the value ofthe new raw data point (e.g., value=120) to update the aggregated valueof the final data sample of average quarter-hour timeseries 706 (i.e.,average(110, 120)=115).

If the new raw data sample has a timestamp that does not occur withinany of the previous aggregation intervals, sample aggregator 608 cancreate a new data sample in average quarter-hour timeseries 706. The newdata sample in average quarter-hour timeseries 706 can have a new datatimestamp defining the beginning of an aggregation interval thatincludes the timestamp of the new raw data sample. For example, if thenew raw data sample has a timestamp of 2016-01-01T01:00:11, sampleaggregator 608 can determine that the new raw data sample does not occurwithin any of the aggregation intervals previously established foraverage quarter-hour timeseries 706. Sample aggregator 608 can generatea new data sample in average quarter-hour timeseries 706 with thetimestamp 2016-01-01T01:00:00 and can calculate the value of the newdata sample in average quarter-hour timeseries 706 based on the value ofthe new raw data sample, as previously described.

Sample aggregator 608 can update the values of the remaining data rolluptimeseries 708-714 in a similar manner. For example, sample aggregator608 determine whether the timestamp of the updated data sample inaverage quarter-hour timeseries is within any of the aggregationintervals for the samples of average hourly timeseries 708. Sampleaggregator 608 can determine that the timestamp 2016-01-01T00:45:00occurs within the aggregation interval beginning at timestamp2016-01-01T00:00:00 for average hourly timeseries 708. Sample aggregator608 can use the updated value of the final data sample of averagequarter-hour timeseries 706 (e.g., value=115) to update the value of thesecond sample of average hourly timeseries 708 (i.e., average(65, 80,95, 115)=88.75). Sample aggregator 608 can use the updated value of thefinal data sample of average hourly timeseries 708 to update the finalsample of average daily timeseries 710 using the same technique.

In some embodiments, sample aggregator 608 updates the aggregated datavalues of data rollup timeseries 706-714 each time a new raw data sampleis received. Updating each time a new raw data sample is receivedensures that the data rollup timeseries 706-714 always reflect the mostrecent data samples. In other embodiments, sample aggregator 608 updatesthe aggregated data values of data rollup timeseries 706-714periodically at predetermined update intervals (e.g., hourly, daily,etc.) using a batch update technique. Updating periodically can be moreefficient and require less data processing than updating each time a newdata sample is received, but can result in aggregated data values thatare not always updated to reflect the most recent data samples.

In some embodiments, sample aggregator 608 is configured to cleanse rawdata timeseries 704. Cleansing raw data timeseries 704 can includediscarding exceptionally high or low data. For example, sampleaggregator 608 can identify a minimum expected data value and a maximumexpected data value for raw data timeseries 704. Sample aggregator 608can discard data values outside this range as bad data. In someembodiments, the minimum and maximum expected values are based onattributes of the data point represented by the timeseries. For example,data point 702 represents a measured outdoor air temperature andtherefore has an expected value within a range of reasonable outdoor airtemperature values for a given geographic location (e.g., between −20°F. and 110° F.). Sample aggregator 608 can discard a data value of 330for data point 702 since a temperature value of 330° F. is notreasonable for a measured outdoor air temperature.

In some embodiments, sample aggregator 608 identifies a maximum rate atwhich a data point can change between consecutive data samples. Themaximum rate of change can be based on physical principles (e.g., heattransfer principles), weather patterns, or other parameters that limitthe maximum rate of change of a particular data point. For example, datapoint 702 represents a measured outdoor air temperature and thereforecan be constrained to have a rate of change less than a maximumreasonable rate of change for outdoor temperature (e.g., five degreesper minute). If two consecutive data samples of the raw data timeseries704 have values that would require the outdoor air temperature to changeat a rate in excess of the maximum expected rate of change, sampleaggregator 608 can discard one or both of the data samples as bad data.

Sample aggregator 608 can perform any of a variety of data cleansingoperations to identify and discard bad data samples. Several examples ofdata cleansing operations which can be performed by sample aggregator608 are described in U.S. patent application Ser. No. 13/631,301 titled“Systems and Methods for Data Quality Control and Cleansing” and filedSep. 28, 2012, the entire disclosure of which is incorporated byreference herein. In some embodiments, sample aggregator 608 performsthe data cleansing operations for raw data timeseries 704 beforegenerating the data rollup timeseries 706-714. This ensures that rawdata timeseries 704 used to generate data rollup timeseries 706-714 doesnot include any bad data samples. Accordingly, the data rolluptimeseries 706-714 do not need to be re-cleansed after the aggregationis performed.

Referring again to FIG. 6, job manager 604 is shown to include a virtualpoint calculator 610. Virtual point calculator 610 is configured tocreate virtual data points and calculate timeseries values for thevirtual data points. A virtual data point is a type of calculated datapoint derived from one or more actual data points. In some embodiments,actual data points are measured data points, whereas virtual data pointsare calculated data points. Virtual data points can be used assubstitutes for actual sensor data when the sensor data desired for aparticular application does not exist, but can be calculated from one ormore actual data points. For example, a virtual data point representingthe enthalpy of a refrigerant can be calculated using actual data pointsmeasuring the temperature and pressure of the refrigerant. Virtual datapoints can also be used to provide timeseries values for calculatedquantities such as efficiency, coefficient of performance, and othervariables that cannot be directly measured.

Virtual point calculator 610 can calculate virtual data points byapplying any of a variety of mathematical operations or functions toactual data points or other virtual data points. For example, virtualpoint calculator 610 can calculate a virtual data point (pointID₃) byadding two or more actual data points (pointID₁ and pointID₂) (e.g.,pointID₃=pointID₁+pointID₂). As another example, virtual pointcalculator 610 can calculate an enthalpy data point (pointID₄) based ona measured temperature data point (pointID₅) and a measured pressuredata point (pointID₆) (e.g., pointID₄=enthalpy(pointID₅, pointID₆)). Insome instances, a virtual data point can be derived from a single actualdata point. For example, virtual point calculator 610 can calculate asaturation temperature (pointID₇) of a known refrigerant based on ameasured refrigerant pressure (pointID₈) (e.g.,pointID₇=T_(sat)(pointID₈)). In general, virtual point calculator 610can calculate the timeseries values of a virtual data point using thetimeseries values of one or more actual data points and/or thetimeseries values of one or more other virtual data points.

In some embodiments, virtual point calculator 610 uses a set of virtualpoint rules to calculate the virtual data points. The virtual pointrules can define one or more input data points (e.g., actual or virtualdata points) and the mathematical operations that should be applied tothe input data point(s) to calculate each virtual data point. Thevirtual point rules can be provided by a user, received from an externalsystem or device, and/or stored in memory 510. Virtual point calculator610 can apply the set of virtual point rules to the timeseries values ofthe input data points to calculate timeseries values for the virtualdata points. The timeseries values for the virtual data points can bestored as optimized timeseries data in local timeseries database 628and/or hosted timeseries database 636.

Virtual point calculator 610 can calculate virtual data points using thevalues of raw data timeseries 704 and/or the aggregated values of thedata rollup timeseries 706-714. In some embodiments, the input datapoints used to calculate a virtual data point are collected at differentsampling times and/or sampling rates. Accordingly, the samples of theinput data points may not be synchronized with each other, which canlead to ambiguity in which samples of the input data points should beused to calculate the virtual data point. Using the data rolluptimeseries 706-714 to calculate the virtual data points ensures that thetimestamps of the input data points are synchronized and eliminates anyambiguity in which data samples should be used.

Referring now to FIG. 8, several timeseries 800, 820, 840, and 860illustrating the synchronization of data samples resulting fromaggregating the raw timeseries data are shown, according to someembodiments. Timeseries 800 and 820 are raw data timeseries. Raw datatimeseries 800 has several raw data samples 802-810. Raw data sample 802is collected at time t₁; raw data sample 804 is collected at time t₂;raw data sample 806 is collected at time t₃; raw data sample 808 iscollected at time t₄; raw data sample 810 is collected at time t₅; andraw data sample 812 is collected at time t₆.

Raw data timeseries 820 also has several raw data samples 822, 824, 826,828, and 830. However, raw data samples, 822-830 are not synchronizedwith raw data samples 802-812. For example, raw data sample 822 iscollected before time t₁; raw data sample 824 is collected between timest₂ and t₃; raw data sample 826 is collected between times t₃ and t₄; rawdata sample 828 is collected between times t₄ and t₅; and raw datasample 830 is collected between times t₅ and t₆. The lack ofsynchronization between data samples 802-812 and raw data samples822-830 can lead to ambiguity in which of the data samples should beused together to calculate a virtual data point.

Timeseries 840 and 860 are data rollup timeseries. Data rolluptimeseries 840 can be generated by sample aggregator 608 by aggregatingraw data timeseries 800. Similarly, data rollup timeseries 860 can begenerated by sample aggregator 608 by aggregating raw data timeseries820. Both raw data timeseries 800 and 820 can be aggregated using thesame aggregation interval. Accordingly, the resulting data rolluptimeseries 840 and 860 have synchronized data samples. For example,aggregated data sample 842 is synchronized with aggregated data sample862 at time t_(1′). Similarly, aggregated data sample 844 issynchronized with aggregated data sample 864 at time t_(2′); aggregateddata sample 846 is synchronized with aggregated data sample 866 at timet_(3′); and aggregated data sample 848 is synchronized with aggregateddata sample 868 at time t_(4′).

The synchronization of data samples in data rollup timeseries 840 and860 allows virtual point calculator 610 to readily identify which of thedata samples should be used together to calculate a virtual point. Forexample, virtual point calculator 610 can identify which of the samplesof data rollup timeseries 840 and 860 have the same timestamp (e.g.,data samples 842 and 862, data samples 844 and 864, etc.). Virtual pointcalculator 610 can use two or more aggregated data samples with the sametimestamp to calculate a timeseries value of the virtual data point. Insome embodiments, virtual point calculator 610 assigns the sharedtimestamp of the input data samples to the timeseries value of thevirtual data point calculated from the input data samples.

Referring again to FIG. 6, job manager 604 is shown to include a weatherpoint calculator 612. Weather point calculator 612 is configured toperform weather-based calculations using the timeseries data. In someembodiments, weather point calculator 612 creates virtual data pointsfor weather-related variables such as cooling degree days (CDD), heatingdegree days (HDD), cooling energy days (CED), heating energy days (HED),and normalized energy consumption. The timeseries values of the virtualdata points calculated by weather point calculator 612 can be stored asoptimized timeseries data in local timeseries database 628 and/or hostedtimeseries database 636.

Weather point calculator 612 can calculate CDD by integrating thepositive temperature difference between the time-varying outdoor airtemperature T_(OA) and the cooling balance point T_(bC) for the buildingas shown in the following equation:

CDD=∫^(period)max{0,(T_(OA)−T_(bC))} dt

where period is the integration period. In some embodiments, the outdoorair temperature T_(OA) is a measured data point, whereas the coolingbalance point T_(bC) is a stored parameter. To calculate CDD for eachsample of the outdoor air temperature T_(OA), weather point calculator612 can multiply the quantity max{0, (T_(OA)−T_(bC))} by the samplingperiod Δt of the outdoor air temperature T_(OA). Weather pointcalculator 612 can calculate CED in a similar manner using outdoor airenthalpy E_(OA) instead of outdoor air temperature T_(OA). Outdoor airenthalpy E_(OA) can be a measured or virtual data point.

Weather point calculator 612 can calculate HDD by integrating thepositive temperature difference between a heating balance point T_(bH)for the building and the time-varying outdoor air temperature T_(OA) asshown in the following equation:

HDD=∫^(period)max{0,(T_(bH)−T_(OA))} dt

where period is the integration period. In some embodiments, the outdoorair temperature T_(OA) is a measured data point, whereas the heatingbalance point T_(bH) is a stored parameter. To calculate HDD for eachsample of the outdoor air temperature T_(OA), weather point calculator612 can multiply the quantity max{0, (T_(bH)−T_(OA))} by the samplingperiod Δt of the outdoor air temperature T_(OA). Weather pointcalculator 612 can calculate HED in a similar manner using outdoor airenthalpy E_(OA) instead of outdoor air temperature T_(OA).

In some embodiments, both virtual point calculator 610 and weather pointcalculator 612 calculate timeseries values of virtual data points.Weather point calculator 612 can calculate timeseries values of virtualdata points that depend on weather-related variables (e.g., outdoor airtemperature, outdoor air enthalpy, outdoor air humidity, outdoor lightintensity, precipitation, wind speed, etc.). Virtual point calculator610 can calculate timeseries values of virtual data points that dependon other types of variables (e.g., non-weather-related variables).Although only a few weather-related variables are described in detailhere, it is contemplated that weather point calculator 612 can calculatevirtual data points for any weather-related variable. Theweather-related data points used by weather point calculator 612 can bereceived as timeseries data from various weather sensors and/or from aweather service.

Still referring to FIG. 6, job manager 604 is shown to include a meterfault detector 614 and a scalable rules engine 606. Meter fault detector614 and scalable rules engine 606 are configured to detect faults intimeseries data. In some embodiments, meter fault detector 614 performsfault detection for timeseries data representing meter data (e.g.,measurements from a sensor), whereas scalable rules engine 606 performsfault detection for other types of timeseries data. Meter fault detector614 and scalable rules engine 606 can detect faults in the rawtimeseries data and/or the optimized timeseries data.

In some embodiments, meter fault detector 614 and scalable rules engine606 receive fault detection rules 620 and/or reasons 622 from analyticsservice 618. Fault detection rules 620 can be defined by a user via arules editor 624 or received from an external system or device viaanalytics web service 618. In various embodiments, fault detection rules620 and reasons 622 can be stored in rules database 632 and reasonsdatabase 634 within local storage 514 and/or rules database 640 andreasons database 642 within hosted storage 516. Meter fault detector 614and scalable rules engine 606 can retrieve fault detection rules 620from local storage 514 or hosted storage and use fault detection rules620 to analyze the timeseries data.

In some embodiments, fault detection rules 620 provide criteria that canbe evaluated by meter fault detector 614 and scalable rules engine 606to detect faults in the timeseries data. For example, fault detectionrules 620 can define a fault as a data value above or below a thresholdvalue. As another example, fault detection rules 620 can define a faultas a data value outside a predetermined range of values. The thresholdvalue and predetermined range of values can be based on the type oftimeseries data (e.g., meter data, calculated data, etc.), the type ofvariable represented by the timeseries data (e.g., temperature,humidity, energy consumption, etc.), the system or device that measuresor provides the timeseries data (e.g., a temperature sensor, a humiditysensor, a chiller, etc.), and/or other attributes of the timeseriesdata.

Meter fault detector 614 and scalable rules engine 606 can apply thefault detection rules 620 to the timeseries data to determine whethereach sample of the timeseries data qualifies as a fault. In someembodiments, meter fault detector 614 and scalable rules engine 606generate a fault detection timeseries containing the results of thefault detection. The fault detection timeseries can include a set oftimeseries values, each of which corresponds to a data sample of thetimeseries data evaluated by meter fault detector 614 and scalable rulesengine 606. In some embodiments, each timeseries value in the faultdetection timeseries includes a timestamp and a fault detection value.The timestamp can be the same as the timestamp of the corresponding datasample of the data timeseries. The fault detection value can indicatewhether the corresponding data sample of the data timeseries qualifiesas a fault. For example, the fault detection value can have a value of“Fault” if a fault is detected and a value of “Not in Fault” if a faultis not detected in the corresponding data sample of the data timeseries.The fault detection timeseries can be stored in local timeseriesdatabase 628 and/or hosted timeseries database 636 along with the rawtimeseries data and the optimized timeseries data.

Referring now to FIGS. 9A-9B, a block diagram and data table 900illustrating the fault detection timeseries is shown, according to someembodiments. In FIG. 9A, job manager 604 is shown receiving a datatimeseries 902 from local storage 514 or hosted storage 516. Datatimeseries 902 can be a raw data timeseries or an optimized datatimeseries. In some embodiments, data timeseries 902 is a timeseries ofvalues of an actual data point (e.g., a measured temperature). In otherembodiments, data timeseries 902 is a timeseries of values of a virtualdata point (e.g., a calculated efficiency). As shown in chart 900, datatimeseries 902 includes a set of data samples. Each data sample includesa timestamp and a value. Most of the data samples have values within therange of 65-66. However, three of the data samples have values of 42.

Job manager 604 can evaluate data timeseries 902 using a set of faultdetection rules 620 to detect faults in data timeseries 902. In variousembodiments, the fault detection can be performed by meter faultdetector 614 (e.g., if data timeseries 902 is meter data) or by scalablerules engine 606 (e.g., if data timeseries 902 is non-meter data). Insome embodiments, job manager 604 determines that the data sampleshaving values of 42 qualify as faults according to the fault detectionrules 620.

Job manager 604 can generate a fault detection timeseries 904 containingthe results of the fault detection. As shown in chart 900, faultdetection timeseries 904 includes a set of data samples. Like datatimeseries 902, each data sample of fault detection timeseries 904includes a timestamp and a value. Most of the values of fault detectiontimeseries 904 are shown as “Not in Fault,” indicating that no fault wasdetected for the corresponding sample of data timeseries 902 (i.e., thedata sample with the same timestamp). However, three of the data samplesin fault detection timeseries 904 have a value of “Fault,” indicatingthat the corresponding sample of data timeseries 902 qualifies as afault. As shown in FIG. 9A, job manager 604 can store fault detectiontimeseries 904 in local storage 514 (e.g., in local timeseries database628) and/or hosted storage 516 (e.g., in hosted timeseries database 636)along with the raw timeseries data and the optimized timeseries data.

Fault detection timeseries 904 can be used by BMS 500 to perform variousfault detection, diagnostic, and/or control processes. In someembodiments, fault detection timeseries 904 is further processed by jobmanager 604 to generate new timeseries derived from fault detectiontimeseries 904. For example, sample aggregator 608 can use faultdetection timeseries 904 to generate a fault duration timeseries. Sampleaggregator 608 can aggregate multiple consecutive data samples of faultdetection timeseries 904 having the same data value into a single datasample. For example, sample aggregator 608 can aggregate the first two“Not in Fault” data samples of fault detection timeseries 904 into asingle data sample representing a time period during which no fault wasdetected. Similarly, sample aggregator 608 can aggregate the final two“Fault” data samples of fault detection timeseries 904 into a singledata sample representing a time period during which a fault wasdetected.

In some embodiments, each data sample in the fault duration timeserieshas a fault occurrence time and a fault duration. The fault occurrencetime can be indicated by the timestamp of the data sample in the faultduration timeseries. Sample aggregator 608 can set the timestamp of eachdata sample in the fault duration timeseries equal to the timestamp ofthe first data sample in the series of data samples in fault detectiontimeseries 904 which were aggregated to form the aggregated data sample.For example, if sample aggregator 608 aggregates the first two “Not inFault” data samples of fault detection timeseries 904, sample aggregator608 can set the timestamp of the aggregated data sample to2015-12-31T23:10:00. Similarly, if sample aggregator 608 aggregates thefinal two “Fault” data samples of fault detection timeseries 904, sampleaggregator 608 can set the timestamp of the aggregated data sample to2015-12-31T23:50:00.

The fault duration can be indicated by the value of the data sample inthe fault duration timeseries. Sample aggregator 608 can set the valueof each data sample in the fault duration timeseries equal to theduration spanned by the consecutive data samples in fault detectiontimeseries 904 which were aggregated to form the aggregated data sample.Sample aggregator 608 can calculate the duration spanned by multipleconsecutive data samples by subtracting the timestamp of the first datasample of fault detection timeseries 904 included in the aggregationfrom the timestamp of the next data sample of fault detection timeseries904 after the data samples included in the aggregation.

For example, if sample aggregator 608 aggregates the first two “Not inFault” data samples of fault detection timeseries 904, sample aggregator608 can calculate the duration of the aggregated data sample bysubtracting the timestamp 2015-12-31T23:10:00 (i.e., the timestamp ofthe first “Not in Fault” sample) from the timestamp 2015-12-31T23:30:00(i.e., the timestamp of the first “Fault” sample after the consecutive“Not in Fault” samples) for an aggregated duration of twenty minutes.Similarly, if sample aggregator 608 aggregates the final two “Fault”data samples of fault detection timeseries 904, sample aggregator 608can calculate the duration of the aggregated data sample by subtractingthe timestamp 2015-12-31T23:50:00 (i.e., the timestamp of the first“Fault” sample included in the aggregation) from the timestamp2016-01-01T00:10:00 (i.e., the timestamp of the first “Not in Fault”sample after the consecutive “Fault” samples) for an aggregated durationof twenty minutes.

Referring now to FIG. 9C, a flow diagram illustrating how varioustimeseries can be generated, stored, and used in BMS 500 is shown,according to some embodiments. Data collector 512 is shown receivingdata samples from building subsystems 428. In some embodiments, the datasamples include data values for various data points. The data values canbe measured or calculated values, depending on the type of data point.For example, a data point received from a temperature sensor can includea measured data value indicating a temperature measured by thetemperature sensor. A data point received from a chiller controller caninclude a calculated data value indicating a calculated efficiency ofthe chiller. Data collector 512 can receive data samples from multipledifferent devices within building subsystems 428.

In some embodiments, each data sample is received with a timestampindicating a time at which the corresponding data value was measured orcalculated. In other embodiments, data collector 512 adds timestamps tothe data samples based on the times at which the data samples arereceived. Data collector 512 can generate raw timeseries data for eachof the data points for which data samples are received. Each timeseriescan include a series of data values for the same data point and atimestamp for each of the data values. For example, a timeseries for adata point provided by a temperature sensor can include a series oftemperature values measured by the temperature sensor and thecorresponding times at which the temperature values were measured.

Data collector 512 can add timestamps to the data samples or modifyexisting timestamps such that each data sample includes a localtimestamp. Each local timestamp indicates the local time at which thecorresponding data sample was measured or collected and can include anoffset relative to universal time. The local timestamp indicates thelocal time at the location the data point was measured at the time ofmeasurement. The offset indicates the difference between the local timeand a universal time (e.g., the time at the international date line).For example, a data sample collected in a time zone that is six hoursbehind universal time can include a local timestamp (e.g.,Timestamp=2016-03-18T14:10:02) and an offset indicating that the localtimestamp is six hours behind universal time (e.g., Offset=−6:00). Theoffset can be adjusted (e.g., +1:00 or −1:00) depending on whether thetime zone is in daylight savings time when the data sample is measuredor collected. Data collector 512 can provide the raw timeseries data tocontrol applications 536, data cleanser 644, and/or store the rawtimeseries data in timeseries storage 515 (i.e., local storage 514and/or hosted storage 516).

Data cleanser 644 can retrieve the raw data timeseries from timeseriesstorage 515 and cleanse the raw data timeseries. Cleansing the raw datatimeseries can include discarding exceptionally high or low data. Forexample, data cleanser 644 can identify a minimum expected data valueand a maximum expected data value for the raw data timeseries. Datacleanser 644 can discard data values outside this range as bad data. Insome embodiments, the minimum and maximum expected values are based onattributes of the data point represented by the timeseries. For example,an outdoor air temperature data point may have an expected value withina range of reasonable outdoor air temperature values for a givengeographic location (e.g., between −20° F. and 110° F.).

In some embodiments, data cleanser 644 identifies a maximum rate atwhich a data point can change between consecutive data samples. Themaximum rate of change can be based on physical principles (e.g., heattransfer principles), weather patterns, or other parameters that limitthe maximum rate of change of a particular data point. For example, anoutdoor air temperature data point can be constrained to have a rate ofchange less than a maximum reasonable rate of change for outdoortemperature (e.g., five degrees per minute). If two consecutive datasamples of the raw data timeseries have values that would require theoutdoor air temperature to change at a rate in excess of the maximumexpected rate of change, data cleanser 644 can discard one or both ofthe data samples as bad data.

Data cleanser 644 can perform any of a variety of data cleansingoperations to identify and discard bad data samples. Several examples ofdata cleansing operations which can be performed by data cleanser 644are described in U.S. patent application Ser. No. 13/631,301 titled“Systems and Methods for Data Quality Control and Cleansing” and filedSep. 28, 2012, the entire disclosure of which is incorporated byreference herein. In some embodiments, data cleanser 644 performs thedata cleansing operations for the raw data timeseries before sampleaggregator 608 generates the data rollup timeseries. This ensures thatthe raw data timeseries used to generate the data rollup timeseries doesnot include any bad data samples. Accordingly, the data rolluptimeseries do not need to be re-cleansed after the aggregation isperformed. Data cleanser 644 can provide the cleansed timeseries data tocontrol applications 536, sample aggregator 608, and/or store thecleansed timeseries data in timeseries storage 515.

Sample aggregator 608 can retrieve any data timeseries from timeseriesstorage 515 (e.g., a raw data timeseries, a cleansed data timeseries, adata rollup timeseries, a fault detection timeseries, etc.) and generatedata rollup timeseries based on the retrieved data timeseries. For eachdata point, sample aggregator 608 can aggregate a set of data valueshaving timestamps within a predetermined time interval (e.g., aquarter-hour, an hour, a day, etc.) to generate an aggregate data valuefor the predetermined time interval. For example, the raw timeseriesdata for a particular data point may have a relatively short interval(e.g., one minute) between consecutive samples of the data point. Sampleaggregator 608 can generate a data rollup from the raw timeseries databy aggregating all of the samples of the data point having timestampswithin a relatively longer interval (e.g., a quarter-hour) into a singleaggregated value that represents the longer interval.

For some types of timeseries, sample aggregator 608 performs theaggregation by averaging all of the samples of the data point havingtimestamps within the longer interval. Aggregation by averaging can beused to calculate aggregate values for timeseries of non-cumulativevariables such as measured value. For other types of timeseries, sampleaggregator 608 performs the aggregation by summing all of the samples ofthe data point having timestamps within the longer interval. Aggregationby summation can be used to calculate aggregate values for timeseries ofcumulative variables such as the number of faults detected since theprevious sample.

Sample aggregator 608 can generate any type of data rollup timeseriesincluding, for example, an average quarter-hour timeseries, an averagehourly timeseries, an average daily timeseries, an average monthlytimeseries, and an average yearly timeseries, or any other type of datarollup timeseries as described with reference to FIGS. 6-8. Each of thedata rollup timeseries may be dependent upon a parent timeseries. Insome embodiments, sample aggregator 608 updates the aggregated datavalues of data rollup timeseries each time a new raw data sample isreceived and/or each time the parent timeseries is updated. Sampleaggregator 608 can provide the data rollup timeseries to controlapplications 536, virtual point calculator 610, and/or store the datarollup timeseries in timeseries storage 515.

Virtual point calculator 610 can retrieve any timeseries from timeseriesstorage 515 and generate virtual point timeseries using the retrieveddata timeseries. Virtual point calculator can create virtual data pointsand calculate timeseries values for the virtual data points. A virtualdata point is a type of calculated data point derived from one or moreactual data points. In some embodiments, actual data points are measureddata points, whereas virtual data points are calculated data points.Virtual data points can be used as substitutes for actual sensor datawhen the sensor data desired for a particular application does notexist, but can be calculated from one or more actual data points. Forexample, a virtual data point representing the enthalpy of a refrigerantcan be calculated using actual data points measuring the temperature andpressure of the refrigerant. Virtual data points can also be used toprovide timeseries values for calculated quantities such as efficiency,coefficient of performance, and other variables that cannot be directlymeasured.

Virtual point calculator 610 can calculate virtual data points byapplying any of a variety of mathematical operations or functions toactual data points and/or other virtual data points. For example,virtual point calculator 610 can calculate a virtual data point(pointID₃) by adding two or more actual data points (pointID₁ andpointID₂) (e.g., pointID₃=pointID₁+pointID₂). As another example,virtual point calculator 610 can calculate an enthalpy data point(pointID₄) based on a measured temperature data point (pointID₅) and ameasured pressure data point (pointID₆) (e.g.,pointID₄=enthalpy(pointID₅, pointID₆)).

In some instances, a virtual data point can be derived from a singleactual data point. For example, virtual point calculator 610 cancalculate a saturation temperature (pointID₇) of a known refrigerantbased on a measured refrigerant pressure (pointID₈) (e.g.,pointID₇=T_(sat)(pointID₈)). In general, virtual point calculator 610can calculate the timeseries values of a virtual data point using thetimeseries values of one or more actual data points and/or thetimeseries values of one or more other virtual data points. In someembodiments, virtual point calculator 610 automatically updates thevalues of the virtual point timeseries whenever the source data used tocalculate the virtual data points is updated. Virtual point calculator610 can provide the virtual point timeseries to control applications536, scalable rules engine 606, and/or store the virtual pointtimeseries in timeseries storage 515.

Scalable rules engine 606 can retrieve any timeseries from timeseriesstorage 515 and generate fault detection timeseries using the retrieveddata timeseries. Scalable rules engine 606 can apply fault detectionrules to the timeseries data to determine whether each sample of thetimeseries data qualifies as a fault. In some embodiments, scalablerules engine 606 generates a fault detection timeseries containing theresults of the fault detection, as described with reference to FIGS.9A-9B. The fault detection timeseries can include a set of timeseriesvalues, each of which corresponds to a data sample of the timeseriesdata evaluated by scalable rules engine 606.

In some embodiments, each timeseries value in the fault detectiontimeseries includes a timestamp and a fault detection value. Thetimestamp can be the same as the timestamp of the corresponding datasample of the data timeseries. The fault detection value can indicatewhether the corresponding data sample of the data timeseries qualifiesas a fault. For example, the fault detection value can have a value of“Fault” if a fault is detected and a value of “Not in Fault” if a faultis not detected in the corresponding data sample of the data timeseries.In some embodiments, scalable rules engine 606 uses the fault detectiontimeseries to generate derivative timeseries such as a fault durationtimeseries, as described with reference to FIGS. 9A-9B. Scalable rulesengine 606 can provide the fault detection timeseries to controlapplications 536 and/or store the fault detection timeseries intimeseries storage 515.

Each of the data platform services 520 (e.g., data cleanser 644, sampleaggregator 608, virtual point calculator 610, scalable rules engine 606,etc.) can read any data timeseries from timeseries storage 515, generatenew data timeseries (e.g., cleansed data timeseries, data rolluptimeseries, virtual point timeseries, fault detection timeseries, etc.),and store the new data timeseries in timeseries storage 515. The newtimeseries can be stored alongside the original timeseries upon whichthe new timeseries is based such that the original timeseries does notneed to be updated. This allows multiple services to concurrently readthe same data timeseries from timeseries storage 515 without requiringany service to lock the timeseries.

The timeseries stored in timeseries storage 515 can affect each other.For example, the values of one or more first data timeseries can affectthe values of one or more second data timeseries based on the first datatimeseries. The first and second data timeseries can be any of the rawdata timeseries, cleansed data timeseries, data rollup timeseries,virtual point timeseries, fault detection timeseries, or any othertimeseries generated by data platform services 520. When the firsttimeseries is/are updated, the second timeseries can be automaticallyupdated by data platform services 520. Updates to the second timeseriescan trigger automatic updates to one or more third data timeseries basedon the second data timeseries. It is contemplated that any datatimeseries can be based on any other data timeseries and can beautomatically updated when the base data timeseries is updated.

In operation, a raw data timeseries can be written to timeseries storage515 by data collector 512 as the data are collected or received frombuilding subsystems 428. Subsequent processing by data cleanser 644,sample aggregator 608, virtual point calculator 610, and scalable rulesengine 606 can occur in any order. For example, data cleanser 644 cancleanse the raw data timeseries, a data rollup timeseries, a virtualpoint timeseries, and/or a fault detection timeseries. Similarly, sampleaggregator 608 can generate a data rollup timeseries using a raw datatimeseries, a cleansed data timeseries, another data rollup timeseries,a virtual point timeseries, and/or a fault detection timeseries. Virtualpoint calculator 610 can generate a virtual point timeseries based onone or more raw data timeseries, cleansed data timeseries, data rolluptimeseries, other virtual point timeseries, and/or fault detectiontimeseries. Scalable rules engine 606 can generate a fault detectiontimeseries using one or more raw data timeseries, cleansed datatimeseries, data rollup timeseries, virtual point timeseries, and/orother fault detection timeseries.

Referring again to FIG. 6, analytics service 524 is shown to include ananalytics web service 618, fault detection rules 620 and reasons 622, arules editor 624, and an analytics storage interface 626. Analytics webservice 618 is configured to interact with web-based applications tosend and/or receive fault detection rules 620 and reasons 622 andresults of data analytics. In some embodiments, analytics web service618 receives fault detection rules 620 and reasons 622 from a web-basedrules editor 624. For example, if rules editor 624 is a web-basedapplication, analytics web service 618 can receive rules 620 and reasons622 from rules editor 624. In some embodiments, analytics web service618 provides results of the analytics to web-based applications. Forexample, if one or more of applications 530 are web-based applications,analytics web service 618 can provide fault detection timeseries to theweb-based applications.

Analytics storage interface 626 is configured to interact with localstorage 514 and/or hosted storage 516. For example, analytics storageinterface 626 can retrieve rules 620 from local rules database 632within local storage 514 or from hosted rules database 636 within hostedstorage 516. Similarly, analytics storage interface 626 can retrievereasons 622 from local reasons database 634 within local storage 514 orfrom hosted reasons database 642 within hosted storage 516. Analyticsstorage interface 626 can also store rules 620 and reasons 622 withinlocal storage 514 and/or hosted storage 516.

Entity Graph

Referring now to FIG. 10A, an entity graph 1000 is shown, according tosome embodiments. In some embodiments, entity graph 1000 is generated orused by data collector 512, as described with reference to FIG. 5.Entity graph 1000 describes how a building is organized and how thedifferent systems and spaces within the building relate to each other.For example, entity graph 1000 is shown to include an organization 1002,a space 1004, a system 1006, a point 1008, and a timeseries 1009. Thearrows interconnecting organization 1002, space 1004, system 1006, point1008, and timeseries 1009 identify the relationships between suchentities. In some embodiments, the relationships are stored asattributes of the entity described by the attribute.

Organization 1002 is shown to include a contains descendants attribute1010, a parent ancestors attribute 1012, a contains attribute 1014, alocated in attribute 1016, an occupied by ancestors attribute 1018, andan occupies by attribute 1022. The contains descendants attribute 1010identifies any descendant entities contained within organization 1002.The parent ancestors attribute 1012 identifies any parent entities toorganization 1002. The contains attribute 1014 identifies any otherorganizations contained within organization 1002. The asterisk alongsidethe contains attribute 1014 indicates that organization 1002 can containany number of other organizations. The located in attribute 1016identifies another organization within which organization 1002 islocated. The number 1 alongside the located in attribute 1016 indicatesthat organization 1002 can be located in exactly one other organization.The occupies attribute 1022 identifies any spaces occupied byorganization 1002. The asterisk alongside the occupies attribute 1022indicates that organization 1002 can occupy any number of spaces.

Space 1004 is shown to include an occupied by attribute 1020, anoccupied by ancestors attribute 1018, a contains space descendantsattribute 1024, a located in ancestors attribute 1026, a contains spacesattribute 1028, a located in attribute 1030, a served by systemsattribute 1038, and a served by system descendants attribute 1034. Theoccupied by attribute 1020 identifies an organization occupied by space1004. The number 1 alongside the occupied by attribute 1020 indicatesthat space 1004 can be occupied by exactly one organization. Theoccupied by ancestors attribute 1018 identifies one or more ancestors toorganization 1002 that are occupied by space 1004. The asteriskalongside the occupied by ancestors attribute 1018 indicates that space1004 can be occupied by any number of ancestors.

The contains space descendants attribute 1024 identifies any descendantsto space 1004 that are contained within space 1004. The located inancestors attribute 1026 identifies any ancestors to space 1004 withinwhich space 1004 is located. The contains spaces attribute 1028identifies any other spaces contained within space 1004. The asteriskalongside the contains spaces attribute 1028 indicates that space 1004can contain any number of other spaces. The located in attribute 1030identifies another space within which space 1004 is located. The number1 alongside the located in attribute 1030 indicates that space 1004 canbe located in exactly one other space. The served by systems attribute1038 identifies any systems that serve space 1004. The asteriskalongside the served by systems attribute 1038 indicates that space 1004can be served by any number of systems. The served by system descendantsattribute 1034 identifies any descendent systems that serve space 1004.The asterisk alongside the served by descendant systems attribute 1034indicates that space 1004 can be served by any number of descendantsystems.

System 1006 is shown to include a serves spaces attribute 1036, a servesspace ancestors attribute 1032, a subsystem descendants attribute 1040,a part of ancestors attribute 1042, a subsystems attribute 1044, a partof attribute 1046, and a points attribute 1050. The serves spacesattribute 1036 identifies any spaces that are served by system 1006. Theasterisk alongside the serves spaces attribute 1036 indicates thatsystem 1006 can serve any number of spaces. The serves space ancestorsattribute 1032 identifies any ancestors to space 1004 that are served bysystem 1006. The asterisk alongside the serves ancestor spaces attribute1032 indicates that system 1006 can serve any number of ancestor spaces.

The subsystem descendants attribute 1040 identifies any subsystemdescendants of other systems contained within system 1006. The part ofancestors attribute 1042 identifies any ancestors to system 1006 thatsystem 1006 is part of. The subsystems attribute 1044 identifies anysubsystems contained within system 1006. The asterisk alongside thesubsystems attribute 1044 indicates that system 1006 can contain anynumber of subsystems. The part of attribute 1046 identifies any othersystems that system 1006 is part of. The number 1 alongside the part ofattribute 1046 indicates that system 1006 can be part of exactly oneother system. The points attribute 1050 identifies any data points thatare associated with system 1006. The asterisk alongside the pointsattribute 1050 indicates that any number of data points can beassociated with system 1006.

Point 1008 is shown to include a used by system attribute 1048. Theasterisk alongside the used by system attribute 1048 indicates thatpoint 1008 can be used by any number of systems. Point 1008 is alsoshown to include a used by timeseries attribute 1054. The asteriskalongside the used by timeseries attribute 1054 indicates that point1008 can be used by any number of timeseries (e.g., raw data timeseriesvirtual point timeseries, data rollup timeseries, etc.). For example,multiple virtual point timeseries can be based on the same actual datapoint 1008. In some embodiments, the used by timeseries attribute 1054is treated as a list of timeseries that subscribe to changes in value ofdata point 1008. When the value of point 1008 changes, the timeserieslisted in the used by timeseries attribute 1054 can be identified andautomatically updated to reflect the changed value of point 1008.

Timeseries 1009 is shown to include a uses point attribute 1052. Theasterisk alongside the uses point attribute 1052 indicates thattimeseries 1009 can use any number of actual data points. For example, avirtual point timeseries can be based on multiple actual data points. Insome embodiments, the uses point attribute 1052 is treated as a list ofpoints to monitor for changes in value. When any of the pointsidentified by the uses point attribute 1052 are updated, timeseries 1009can be automatically updated to reflect the changed value of the pointsused by timeseries 1009.

Timeseries 1009 is also shown to include a used by timeseries attribute1056 and a uses timeseries attribute 1058. The asterisks alongside theused by timeseries attribute 1056 and the uses timeseries attribute 1058indicate that timeseries 1009 can be used by any number of othertimeseries and can use any number of other timeseries. For example, botha data rollup timeseries and a virtual point timeseries can be based onthe same raw data timeseries. As another example, a single virtual pointtimeseries can be based on multiple other timeseries (e.g., multiple rawdata timeseries). In some embodiments, the used by timeseries attribute1056 is treated as a list of timeseries that subscribe to updates intimeseries 1009. When timeseries 1009 is updated, the timeseries listedin the used by timeseries attribute 1056 can be identified andautomatically updated to reflect the change to timeseries 1009.Similarly, the uses timeseries attribute 1058 can be treated as a listof timeseries to monitor for updates. When any of the timeseriesidentified by the uses timeseries attribute 1058 are updated, timeseries1009 can be automatically updated to reflect the updates to the othertimeseries upon which timeseries 1009 is based.

Referring now to FIG. 10B, an example of an entity graph 1060 for aparticular building management system is shown, according to someembodiments. Entity graph 1060 is shown to include an organization 1061(“ACME Corp”). Organization 1061 be a collection of people, a legalentity, a business, an agency, or other type of organization.Organization 1061 occupies space 1063 (“Milwaukee Campus”), as indicatedby the occupies attribute 1064. Space 1063 is occupied by organization1061, as indicated by the occupied by attribute 1062.

In some embodiments, space 1063 is a top level space in a hierarchy ofspaces. For example, space 1063 can represent an entire campus (i.e., acollection of buildings). Space 1063 can contain various subspaces(e.g., individual buildings) such as space 1065 (“Building 1”) and space1073 (“Building 2”), as indicated by the contains attributes 1068 and1080. Spaces 1065 and 1080 are located in space 1063, as indicated bythe located in attribute 1066. Each of spaces 1065 and 1073 can containlower level subspaces such as individual floors, zones, or rooms withineach building. However, such subspaces are omitted from entity graph1060 for simplicity.

Space 1065 is served by system 1067 (“ElecMainMeter1”) as indicated bythe served by attribute 1072. System 1067 can be any system that servesspace 1065 (e.g., a HVAC system, a lighting system, an electricalsystem, a security system, etc.). The serves attribute 1070 indicatesthat system 1067 serves space 1065. In entity graph 1060, system 1067 isshown as an electrical system having a subsystem 1069(“LightingSubMeter1”) and a subsystem 1071 (“PlugLoadSubMeter2”) asindicated by the subsystem attributes 1076 and 1078. Subsystems 1069 and1071 are part of system 1067, as indicated by the part of attribute1074.

Space 1073 is served by system 1075 (“ElecMainMeter2”) as indicated bythe served by attribute 1084. System 1075 can be any system that servesspace 1073 (e.g., a HVAC system, a lighting system, an electricalsystem, a security system, etc.). The serves attribute 1082 indicatesthat system 1075 serves space 1073. In entity graph 1060, system 1075 isshown as an electrical system having a subsystem 1077(“LightingSubMeter3”) as indicated by the subsystem attribute 1088.Subsystem 1077 is part of system 1075, as indicated by the part ofattribute 1086.

In addition to the attributes shown in FIG. 10B, entity graph 1060 caninclude “ancestors” and “descendants” attributes on each entity in thehierarchy. The ancestors attribute can identify (e.g., in a flat list)all of the entities that are ancestors to a given entity. For example,the ancestors attribute for space 1065 may identify both space 1063 andorganization 1061 as ancestors. Similarly, the descendants attribute canidentify all (e.g., in a flat list) of the entities that are descendantsof a given entity. For example, the descendants attribute for space 1065may identify system 1067, subsystem 1069, and subsystem 1071 asdescendants. This provides each entity with a complete listing of itsancestors and descendants, regardless of how many levels are included inthe hierarchical tree. This is a form of transitive closure.

In some embodiments, the transitive closure provided by the descendantsand ancestors attributes allows entity graph 1060 to facilitate simplequeries without having to search multiple levels of the hierarchicaltree. For example, the following query can be used to find all metersunder the Milwaukee Campus space 1063:

-   -   /Systems?$filter=(systemType eq Jci.Be.Data.SystemType‘Meter’)        and ancestorSpaces/any(a:a/name eq ‘Milwaukee Campus’)        and can be answered using only the descendants attribute of the        Milwaukee Campus space 1063. For example, the descendants        attribute of space 1063 can identify all meters that are        hierarchically below space 1063. The descendants attribute can        be organized as a flat list and stored as an attribute of space        1063. This allows the query to be served by searching only the        descendants attribute of space 1063 without requiring other        levels or entities of the hierarchy to be searched.

Referring now to FIG. 11, an object relationship diagram 1100 is shown,according to some embodiments. Relationship diagram 1100 is shown toinclude an entity template 1102, a point 1104, a timeseries 1106, and asample 1108. In some embodiments, entity template 1102, point 1104,timeseries 1106, and sample 1108 are stored as data objects withinmemory 510, local storage 514, and/or hosted storage 516. Relationshipdiagram 1100 illustrates the relationships between entity template 1102,point 1104, and timeseries 1106.

Entity template 1102 can include various attributes such as an IDattribute, a name attribute, a properties attribute, and a relationshipsattribute. The ID attribute can be provided as a text string andidentifies a unique ID for entity template 1102. The name attribute canalso be provided as a text string and identifies the name of entitytemplate 1102. The properties attribute can be provided as a vector andidentifies one or more properties of entity template 1102. Therelationships attribute can also be provided as a vector and identifiesone or more relationships of entity template 1102.

Point 1104 can include various attributes such as an ID attribute, anentity template ID attribute, a timeseries attribute, and a units IDattribute. The ID attribute can be provided as a text string andidentifies a unique ID for point 1104. The entity template ID attributecan also be provided as a text string and identifies the entity template1102 associated with point 1104 (e.g., by listing the ID attribute ofentity template 1102). Any number of points 1104 can be associated withentity template 1102. However, in some embodiments, each point 11104 isassociated with a single entity template 1102. The timeseries attributecan be provided as a text string and identifies any timeseriesassociated with point 1104 (e.g., by listing the ID string of anytimeseries 1106 associated with point 1104). The units ID attribute canalso be provided as a text string and identifies the units of thevariable quantified by point 1104.

Timeseries 1106 can include various attributes such as an ID attribute,a samples attribute, a transformation type attribute, and a units IDattribute. The ID attribute can be provided as a text string andidentifies a unique ID for timeseries 1106. The unique ID of timeseries1106 can be listed in the timeseries attribute of point 1104 toassociate timeseries 1106 with point 1104. Any number of timeseries 1106can be associated with point 1104. Each timeseries 1106 is associatedwith a single point 1104. The samples attribute can be provided as avector and identifies one or more samples associated with timeseries1106. The transformation type attribute identifies the type oftransformation used to generate timeseries 1106 (e.g., average hourly,average daily, average monthly, etc.). The units ID attribute can alsobe provided as a text string and identifies the units of the variablequantified by timeseries 1106.

Sample 1108 can include a timestamp attribute and a value attribute. Thetimestamp attribute can be provided in local time and can include anoffset relative to universal time. The value attribute can include adata value of sample 1108. In some instances, the value attribute is anumerical value (e.g., for measured variables). In other instances, thevalue attribute can be a text string such as “Fault” if sample 1108 ispart of a fault detection timeseries.

Dashboard Layouts

Referring now to FIG. 12, a block diagram illustrating the operation ofdashboard layout generator 518 is shown, according to some embodiments.Dashboard layout generator 518 is shown receiving points 1202, rawtimeseries data 1204, and optimized timeseries data 1206. Points 1202can include actual data points (e.g., measured data points), virtualdata points (e.g., calculated data points) or other types of data pointsfor which sample data is received at BMS 500 or calculated by BMS 500.Points 1202 can include instances of point 1104, as described withreference to FIG. 11. For example, each of points 1202 can include apoint ID, an entity template ID, an indication of one or more timeseriesassociated with the point, and a units ID. Raw timeseries data 1204 caninclude the raw timeseries data collected or generated by data collector512. Optimized timeseries data 1206 can include data rollup timeseries,cleansed timeseries, virtual point timeseries, weather point timeseries,fault detection timeseries, and/or other types of timeseries data whichcan be generated or processed by job manager 604.

Dashboard layout generator 518 is shown generating a dashboard layoutdescription 1208. In some embodiments, dashboard layout description 1208is a framework agnostic layout description which can be used to render auser interface (i.e., a dashboard layout) by a variety of differentrendering engines (e.g., a web browser, a PDF engine, etc.) and/orframeworks. Dashboard layout description 1208 is not itself a userinterface, but rather a schema which can be used by applications 530 andother frameworks to generate a user interface. Many different frameworksand applications 530 can read and use dashboard layout description 1208to generate a user interface according to the theming and sizing of theframework. In some embodiments, dashboard layout description 1208describes the dashboard layout using a grid of rows and columns.

Referring now to FIG. 13, a grid 1300 illustrating dashboard layoutdescription 1208 is shown. Grid 1300 is shown as a m×n grid including mrows and n columns. The intersections of the rows and columns defineparticular locations in grid 1300 at which widgets can be located. Forexample, grid 1300 is shown to include a text widget 1302 at theintersection of the first row and the second column. Grid 1300 alsoincludes a graph widget 1304 at the intersection of the second row andthe second column. In some embodiments, the locations of widgets 1302and 1304 are defined by the row and column indices of grid 1300. Forexample, dashboard layout description 1208 can define the location oftext widget 1302 by specifying that text widget 1302 is located at theintersection of the first row and the second column of grid 1300.Similarly, dashboard layout description 1208 can define the location ofgraph widget 1304 by specifying that graph widget 1304 is located at theintersection of the second row and the second column of grid 1300.

In some embodiments, dashboard layout description 1208 defines variousproperties for each widget. For example, widgets 1302 and 1304 can havea widget type property defining the type of the widget (e.g., text,graph, image, etc.). In some embodiments, widget 1302 has a textproperty defining the text displayed by widget 1302. Widget 1304 caninclude graph properties that define various attributes of the graph(e.g., graph title, x-axis title, y-axis title, etc.). In someembodiments, graph widget 1304 includes a property that defines one ormore timeseries of data displayed in widget 1304. The timeseries can bedifferent timeseries of the same data point (e.g., a raw datatimeseries, an average hourly timeseries, an average daily timeseries,etc.) or timeseries of different data points. In some embodiments, graphwidget 1304 includes properties that defines the widget name and a setof APIs that drive widget 1304 (e.g., service URLs or database URLs).

In some embodiments, dashboard layout description 1208 includes a toplevel dashboard element containing properties that apply to the entiredashboard layout. Such properties can include, for example, dashboardname, whether the widgets are collapsible, whether the dashboard iseditable, and the grid layout. The grid layout can be defined as anarray of objects (e.g., widgets), each of which is an array ofproperties. The dashboard layout can be static, dynamic, or userspecific. Static layouts can be used when the layout does not change.Dynamic layouts can be used to add more features to an existingdashboard. User specified layouts can be used to allow the dashboard tobe adjusted by the user (e.g., by adding or removing widgets).

Dashboard layout description 1208 can be used to drive various services.In some embodiments, dashboard layout description 1208 enables providinga user interface as a service. In this scenario, dashboard layoutgenerator 518 can provide a framework with predefined widgets. Theframework can read dashboard layout description 1208 and render the userinterface. Providing the user interface as a service allows new widgetsto be added to the predefined widgets. In other embodiments, dashboardlayout description 1208 enables providing data visualization as aservice.

Referring now to FIGS. 14-15, an example of a dashboard layoutdescription 1400 and a dashboard layout 1500 that can be generated fromdashboard layout description 1400 are shown, according to someembodiments. Referring particularly to FIG. 14, dashboard layoutdescription 1400 is shown to include several properties 1402 that applyto the entire dashboard layout 1500. Properties 1402 are shown toinclude a name of dashboard layout 1500 and properties defining whetherdashboard layout 1500 is collapsible, maximizable, and/or editable.

In some embodiments, dashboard layout description 1400 is described inJSON format. For example, dashboard layout description 1400 is shown toinclude a rows object 1404 and a columns object 1406 contained withinrows object 1404. Columns object 1406 contains two elements.Accordingly, dashboard layout description 1400 defines a layout thatincludes a single row and two columns within the row. Each of thecolumns includes a widget. For example, the first element of columnsobject 1406 includes a first widget object 1408, whereas the secondelement of columns object 1406 includes a second widget object 1410.

Widget object 1408 includes several properties 1412 defining variousattributes of widget object 1408. For example, widget object 1408 isshown to include properties defining a widget name (i.e., MEMS Meter), awidget type (i.e., spline) and a widget configuration. The spline typeindicates that widget object 1408 defines a line graph. The widgetconfiguration property includes several sub-properties 1414 definingattributes of the line graph. Sub-properties 1414 are shown to include atitle, an x-axis label (i.e., datetime), a y-axis label (i.e., KW), atoken API defining an API that drives widget object 1408, and a sampleAPI defining another API that drives widget object 1408. Sub-properties1414 also include a points property defining several timeseries that canbe displayed in widget object 1408.

Similarly, widget object 1410 includes several properties 1416 definingvarious attributes of widget object 1410. For example, widget object1410 is shown to include properties defining a widget name (i.e., MEMSMeter), a widget type (i.e., column) and a widget configuration. Thecolumn type indicates that widget object 1410 defines a bar graph. Thewidget configuration property includes several sub-properties 1418defining attributes of the bar graph. Sub-properties 1418 are shown toinclude a title, an x-axis label (i.e., datetime), a y-axis label (i.e.,KWH), a token API defining an API that drives widget object 1410, and asample API defining another API that drives widget object 1410.Sub-properties 1418 also include a points property defining severaltimeseries that can be displayed in widget object 1410.

Referring now to FIG. 15, dashboard layout 1500 is shown to include atitle 1502, a first widget 1504, and a second widget 1506. The text oftitle 1502 is defined by properties 1402, whereas first widget 1504 isdefined by widget object 1408, and second widget 1506 is defined bywidget object 1410. Dashboard layout 1500 includes a single row and twocolumns within the row. The first column includes first widget 1504,whereas the second column includes second widget 1506. Widget 1504 isshown to include the title 1508 “MEMS Meter” (defined by properties1412) and a dropdown selector 1512 which can be used to select any ofthe timeseries defined by sub-properties 1414. Similarly, widget 1506 isshown to include the title 1510 “MEMS Meter” (defined by properties1416) and a dropdown selector 1514 which can be used to select any ofthe timeseries defined by sub-properties 1418.

Referring now to FIGS. 16-17, another example of a dashboard layoutdescription 1600 and a dashboard layout 1700 that can be generated fromdashboard layout description 1600 are shown, according to someembodiments. Referring particularly to FIG. 16, dashboard layoutdescription 1600 is shown to include several properties 1602 that applyto the entire dashboard layout 1700. Properties 1602 are shown toinclude a name of dashboard layout 1700 and properties defining whetherdashboard layout 1700 is collapsible, maximizable, and/or editable.

In some embodiments, dashboard layout description 1600 is described inJSON format. For example, dashboard layout description 1600 is shown toinclude a rows object 1604. Rows object 1604 has two data elements, eachdefining a different row of dashboard layout 1700. The first element ofrows object 1604 contains a first a columns object 1606, whereas thesecond element of rows object 1604 contains a second columns object1607. Columns object 1606 has a single element which includes a firstwidget object 1608. However, columns object 1607 has two elements, eachof which includes a widget object (i.e., widget objects 1610 and 1620).Accordingly, dashboard layout description 1600 defines a layout thatincludes a first row with one column and a second row with two columns.The first row contains widget object 1608. The second row contains twowidget objects 1610 and 1620 in adjacent columns.

Widget object 1608 includes several properties 1612 defining variousattributes of widget object 1608. For example, widget object 1608 isshown to include properties defining a widget name (i.e., BTU Meter), awidget type (i.e., spline) and a widget configuration. The spline typeindicates that widget object 1608 defines a line graph. The widgetconfiguration property includes several sub-properties 1614 definingattributes of the line graph. Sub-properties 1614 are shown to include atitle, an x-axis label, a y-axis label, a token API defining an API thatdrives widget object 1608, and a sample API defining another API thatdrives widget object 1608. Sub-properties 1614 also include a pointsproperty defining several timeseries that can be displayed in widgetobject 1608.

Similarly, widget object 1610 includes several properties 1616 definingvarious attributes of widget object 1610. For example, widget object1610 is shown to include properties defining a widget name (i.e., Meter1), a widget type (i.e., spline) and a widget configuration. The splinetype indicates that widget object 1610 defines a line graph. The widgetconfiguration property includes several sub-properties 1618 definingattributes of the line graph. Sub-properties 1618 are shown to include atitle, an x-axis label, a y-axis label, a token API defining an API thatdrives widget object 1610, and a sample API defining another API thatdrives widget object 1610. Sub-properties 1618 also include a pointsproperty defining several timeseries that can be displayed in widgetobject 1610.

Widget object 1620 includes several properties 1622 defining variousattributes of widget object 1620. For example, widget object 1620 isshown to include properties defining a widget name (i.e., Meter 1), awidget type (i.e., spline) and a widget configuration. The spline typeindicates that widget object 1620 defines a line graph. The widgetconfiguration property includes several sub-properties 1624 definingattributes of the line graph. Sub-properties 1624 are shown to include atitle, an x-axis label, a y-axis label, a token API defining an API thatdrives widget object 1620, and a sample API defining another API thatdrives widget object 1620. Sub-properties 1624 also include a pointsproperty defining several timeseries that can be displayed in widgetobject 1620.

Referring now to FIG. 17, dashboard layout 1700 is shown to include atitle 1702, a first widget 1704, a second widget 1706, and a thirdwidget 1707. The text of title 1702 is defined by properties 1602. Thecontent of first widget 1704 is defined by widget object 1608; thecontent of second widget 1706 is defined by widget object 1610; and thecontent of third widget 1707 is defined by widget object 1620. Dashboardlayout 1700 includes two rows. The first row includes a single column,whereas the second row includes two columns. The first row includesfirst widget 1704, whereas the second row includes second widget 1706 inthe first column and third widget 1707 in the second column.

Widget 1704 is shown to include the title 1708 “BTU Meter” (defined byproperties 1612) and a dropdown selector 1712 which can be used toselect any of the timeseries defined by sub-properties 1614. Similarly,widget 1706 is shown to include the title 1710 “Meter 1” (defined byproperties 1616) and a dropdown selector 1714 which can be used toselect any of the timeseries defined by sub-properties 1618. Widget 1707is shown to include the title 1711 “Meter 1” (defined by properties1622) and a dropdown selector 1715 which can be used to select any ofthe timeseries defined by sub-properties 1624.

CONFIGURATION OF EXEMPLARY EMBODIMENTS

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

1-20. (canceled)
 21. A building management system comprising: one ormore memory devices configured to store instructions thereon that, whenexecuted by one or more processors, cause the one or more processors to:receive raw data samples from building equipment and generate a raw datatimeseries comprising a plurality of the raw data samples; generate adata rollup timeseries comprising a plurality of aggregated data samplesas the raw data samples are received; receive one or more new raw datasamples from the building equipment; determine whether an update istriggered based on the one or more new raw data samples; update the datarollup timeseries with the one or more new raw data samples in responseto a determination that the update is triggered; and store the datarollup timeseries in a database.
 22. The building management system ofclaim 21, wherein the instructions cause the one or more processors tostore one or more raw data samples of the raw data samples with atimestamp, the timestamp comprising: a local time indicating a time atwhich each of the one or more raw data samples was collected in a timezone within which each of the one or more raw data samples wascollected; and a time offset indicating a difference between the localtime and universal time.
 23. The building management system of claim 21,wherein the instructions cause the one or more processors to update thedata rollup timeseries by: identifying a timestamp of a new raw datasample of the one or more new raw data samples; identifying a particularaggregated data sample of the data rollup timeseries that was generatedusing an aggregation interval that includes the timestamp of the new rawdata sample; and recalculating an aggregated data value of theparticular aggregated data sample using the new raw data sample and anyother raw data samples that have timestamps within the aggregationinterval.
 24. The building management system of claim 21, wherein theinstructions cause the one or more processors to update the data rolluptimeseries by: identifying a timestamp of a new raw data sample of theone or more new raw data samples; determining that the timestamp of thenew raw data sample is not within any aggregation interval used togenerate the plurality of aggregated data samples; generating a newaggregated data sample using the new raw data sample and a newaggregation interval that includes the timestamp of the new raw datasample; and adding the new aggregated data sample to the data rolluptimeseries.
 25. The building management system of claim 21, wherein theinstructions cause the one or more processors to: create a virtual datapoint representing a variable not directly measured by the buildingequipment; calculate data values for each of a plurality of samples ofthe virtual data point using at least one of the raw data samples andthe plurality of aggregated data samples; generate a virtual pointtimeseries comprising the plurality of samples of the virtual datapoint; and store the virtual point timeseries in the database.
 26. Thebuilding management system of claim 21, wherein the instructions causethe one or more processors to: detect faults in timeseries data byapplying fault detection rules to at least one of the raw datatimeseries and the data rollup timeseries; generate a fault detectiontimeseries comprising a plurality of fault detection data samples, eachof the plurality of fault detection data samples having a timestamp anda data value indicating whether a fault is detected at the timestamp;and store the fault detection timeseries in the database.
 27. Thebuilding management system of claim 21, wherein: each of the raw datasamples comprises a timestamp and a raw data value; and wherein theinstructions cause the one or more processors to generate the pluralityof aggregated data samples by aggregating one or more of the raw datasamples that have timestamps within a predetermined aggregationinterval.
 28. The building management system of claim 27, whereinaggregating the one or more of the raw data samples comprises averagingone or more raw data values of the one or more of the raw data samples.29. A building management system comprising: one or more memory devicesconfigured to store instructions thereon that, when executed by one ormore processors, cause the one or more processors to: receive raw datasamples from a sensor and generate a raw data timeseries comprising aplurality of the raw data samples; associate the raw data timeserieswith a measured data point; generate a virtual data point representing anon-measured variable; calculate the virtual data point based on themeasured data point and generate a virtual point timeseries comprising aplurality of samples of the virtual data point; and store the virtualpoint timeseries in a database.
 30. The building management system ofclaim 29, wherein the instructions cause the one or more processors tocalculate the virtual data point by: applying values of the measureddata point as inputs to a mathematical function; and evaluating themathematical function to determine corresponding values of the virtualdata point.
 31. The building management system of claim 29, wherein theinstructions cause the one or more processors to calculate the virtualdata point as a function of the measured data point and one or moreother data points.
 32. The building management system of claim 29,wherein the instructions cause the one or more processors to: generate adata rollup timeseries comprising a plurality of aggregated datasamples; and calculate a value for each of the plurality of aggregateddata samples by aggregating one or more of the raw data samples thathave timestamps within a predetermined aggregation interval.
 33. Thebuilding management system of claim 29, wherein the sensor measures aweather-related variable; wherein the instructions cause the one or moreprocessors to: associate the raw data timeseries with a measuredweather-related data point; and calculate the virtual data point as afunction of the measured weather-related data point.
 34. The buildingmanagement system of claim 29, wherein the instructions cause the one ormore processors to: detect faults in timeseries data by applying faultdetection rules to the virtual point timeseries; generate a faultdetection timeseries comprising a plurality of fault detection datasamples, each of the plurality of fault detection data samples having atimestamp and a data value indicating whether a fault is detected at thetimestamp; and store the fault detection timeseries in the database. 35.The building management system of claim 29, wherein the instructionscause the one or more processors to synchronize the raw data timeserieswith an asynchronous timeseries by aggregating both the raw datatimeseries and the asynchronous timeseries using equivalent aggregationintervals.
 36. The building management system of claim 35, wherein theinstructions cause the one or more processors to calculate the virtualdata point by: identifying a plurality of aggregated data valuesgenerated by aggregating the raw data timeseries; identifying, for eachof the plurality of aggregated data values, a corresponding synchronizeddata value generated by aggregating the asynchronous timeseries; andcalculating, for each sample of the virtual data point, a data value ofthe sample by evaluating a function of one of the plurality ofaggregated data values and the corresponding synchronized data value.37. A method of a building management system comprising: receiving, byone or more processing circuits, raw data samples from buildingequipment and generating, by the one or more processing circuits, a rawdata timeseries comprising a plurality of the raw data samples;generating, by the one or more processing circuits, a data rolluptimeseries comprising a plurality of aggregated data samples as the rawdata samples are received; receiving, by the one or more processingcircuits, one or more new raw data samples from the building equipment;determining, by the one or more processing circuits, whether an updateis triggered based on the one or more new raw data samples; updating, bythe one or more processing circuits, the data rollup timeseries with theone or more new raw data samples in response to a determination that theupdate is triggered; and storing, by the one or more processingcircuits, the data rollup timeseries in a database.
 38. The method ofclaim 37, further comprising updating, by the one or more processingcircuits, the data rollup timeseries by: identifying a timestamp of anew raw data sample of the one or more new raw data samples; identifyinga particular aggregated data sample of the data rollup timeseries thatwas generated using an aggregation interval that includes the timestampof the new raw data sample; and recalculating an aggregated data valueof the particular aggregated data sample using the new raw data sampleand any other raw data samples that have timestamps within theaggregation interval.
 39. The method of claim 37, wherein: each of theraw data samples comprises a timestamp and a raw data value; and whereingenerating, by the one or more processing circuits, the plurality ofaggregated data samples comprises aggregating one or more of the rawdata samples that have timestamps within a predetermined aggregationinterval.
 40. The method of claim 39, wherein aggregating the one ormore of the raw data samples comprises averaging one or more raw datavalues of the one or more of the raw data samples.